Latest open access articles published in Journal of Operational and Strategic Analytics at https://www.acadlore.com/journals/JOSA https://www.acadlore.com/journals/JOSA Acadlore en Creative Commons Attribution(CC - BY) JOSA support@acadlore.com https://www.acadlore.com/article/JOSA/2026_4_1/josa040101 With the rapid expansion of e-commerce, last-mile delivery in express logistics faces significant challenges, including low efficiency and high operational costs. Taking the Xiqing District of Tianjin as a case study, this research proposes a three-stage framework integrating complex network theory and machine learning. First, the Louvain algorithm is employed to achieve intelligent partitioning of delivery areas, resulting in a modularity increase to 0.789. Second, an eXtreme Gradient Boosting (XGBoost) model is utilized to predict terminal service modes, achieving an accuracy of 87.8%. Finally, a route planning model is constructed using Particle Swarm Optimization (PSO). To validate these methods, a three-day logistics system simulation was conducted via AnyLogic to evaluate the effectiveness of different delivery policies. The results demonstrate that, compared to traditional independent delivery, the joint delivery approach reduces total costs by 25.32%. Furthermore, by introducing a carbon emission accounting model, leading to an estimated 25% reduction in daily carbon emissions, achieving a win-win situation for both economic and environmental benefits. 01-10-2026 <![CDATA[ <p>With the rapid expansion of e-commerce, last-mile delivery in express logistics faces significant challenges, including low efficiency and high operational costs. Taking the Xiqing District of Tianjin as a case study, this research proposes a three-stage framework integrating complex network theory and machine learning. First, the Louvain algorithm is employed to achieve intelligent partitioning of delivery areas, resulting in a modularity increase to 0.789. Second, an eXtreme Gradient Boosting (XGBoost) model is utilized to predict terminal service modes, achieving an accuracy of 87.8%. Finally, a route planning model is constructed using Particle Swarm Optimization (PSO). To validate these methods, a three-day logistics system simulation was conducted via AnyLogic to evaluate the effectiveness of different delivery policies. The results demonstrate that, compared to traditional independent delivery, the joint delivery approach reduces total costs by 25.32%. Furthermore, by introducing a carbon emission accounting model, leading to an estimated 25% reduction in daily carbon emissions, achieving a win-win situation for both economic and environmental benefits.</p> ]]> Optimization of Last-Mile Joint Delivery in Express Logistics Based on Complex Networks and Machine Learning liang liu penghui zhang jiawei xu doi: 10.56578/josa040101 Journal of Operational and Strategic Analytics 01-10-2026 Journal of Operational and Strategic Analytics 01-10-2026 2026 4 1 Article 1 10.56578/josa040101 https://www.acadlore.com/article/JOSA/2026_4_1/josa040101 https://www.acadlore.com/article/JOSA/2025_3_4/josa030404 The vehicle routing problem with time windows (VRPTW) remains a central challenge in urban logistics due to the interplay between spatial dispersion, operational constraints, and service reliability requirements. To address the scalability limitations and premature convergence issues commonly observed in conventional metaheuristics, a two-stage optimization framework is developed that integrates clustering-based spatial decomposition with an adaptive evolutionary search mechanism. In the first stage, $k$-means clustering is employed to partition large-scale customer nodes into geographically coherent subregions, with the number of clusters determined using the silhouette coefficient to ensure structural consistency. This decomposition reduces problem dimensionality and enables localized route optimization. In the second stage, an adaptive genetic algorithm is designed in which crossover and mutation probabilities are dynamically adjusted according to population fitness distribution, thereby improving global exploration in early iterations and enhancing solution refinement in later stages. A mathematical model is formulated to minimize total operational cost, incorporating vehicle activation, transportation distance, and time window penalties under capacity and service constraints. The proposed framework is evaluated using a real-world logistics dataset involving 60 customer nodes. To assess operational robustness, the optimized routing schemes are further validated within an agent-based simulation environment. Comparative results show that the proposed method achieves consistent improvements over baseline strategies, with cost reductions of 28.12% and 20.62% across two service regions, while significantly increasing vehicle utilization and reducing fleet size. The findings indicate that the integration of spatial decomposition and adaptive evolutionary control provides a practical and scalable solution for complex VRPTW instances in dynamic urban logistics settings. 11-24-2025 <![CDATA[ <p>The vehicle routing problem with time windows (VRPTW) remains a central challenge in urban logistics due to the interplay between spatial dispersion, operational constraints, and service reliability requirements. To address the scalability limitations and premature convergence issues commonly observed in conventional metaheuristics, a two-stage optimization framework is developed that integrates clustering-based spatial decomposition with an adaptive evolutionary search mechanism. In the first stage, $k$-means clustering is employed to partition large-scale customer nodes into geographically coherent subregions, with the number of clusters determined using the silhouette coefficient to ensure structural consistency. This decomposition reduces problem dimensionality and enables localized route optimization. In the second stage, an adaptive genetic algorithm is designed in which crossover and mutation probabilities are dynamically adjusted according to population fitness distribution, thereby improving global exploration in early iterations and enhancing solution refinement in later stages. A mathematical model is formulated to minimize total operational cost, incorporating vehicle activation, transportation distance, and time window penalties under capacity and service constraints. The proposed framework is evaluated using a real-world logistics dataset involving 60 customer nodes. To assess operational robustness, the optimized routing schemes are further validated within an agent-based simulation environment. Comparative results show that the proposed method achieves consistent improvements over baseline strategies, with cost reductions of 28.12% and 20.62% across two service regions, while significantly increasing vehicle utilization and reducing fleet size. The findings indicate that the integration of spatial decomposition and adaptive evolutionary control provides a practical and scalable solution for complex VRPTW instances in dynamic urban logistics settings.</p> ]]> A Two-Stage Clustering–Evolutionary Framework with Adaptive Search Control for the Vehicle Routing Problem with Time Windows liang liu jiarui wang yujing liang doi: 10.56578/josa030404 Journal of Operational and Strategic Analytics 11-24-2025 Journal of Operational and Strategic Analytics 11-24-2025 2025 3 4 Article 251 10.56578/josa030404 https://www.acadlore.com/article/JOSA/2025_3_4/josa030404 https://www.acadlore.com/article/JOSA/2025_3_4/josa030403 Energy supply selection has become a crucial component of organizational strategy, as firms strive to balance sustainability, reliability, and cost efficiency under uncertain market and policy conditions. This study develops a strategic decision-support framework that integrates type-2 fuzzy logic with the Combined Compromise Solution (CoCoSo) method to evaluate alternative energy supply options. The hybrid model addresses the ambiguity inherent in expert judgments by employing type-2 fuzzy sets and prioritizes competing alternatives through the CoCoSo ranking process. Six evaluation criteria—cost, reliability, maintenance, environmental impact, supply stability, and policy support—were defined based on expert consultation. The proposed framework was applied to an industrial case study, demonstrating its capacity to manage conflicting objectives and deliver a transparent, rational ranking of energy alternatives. Sensitivity analysis confirmed the robustness of the results. The findings provide actionable insights for decision-makers and policymakers seeking data-driven strategies to enhance sustainable energy planning and operational efficiency. 11-01-2025 <![CDATA[ Energy supply selection has become a crucial component of organizational strategy, as firms strive to balance sustainability, reliability, and cost efficiency under uncertain market and policy conditions. This study develops a strategic decision-support framework that integrates type-2 fuzzy logic with the Combined Compromise Solution (CoCoSo) method to evaluate alternative energy supply options. The hybrid model addresses the ambiguity inherent in expert judgments by employing type-2 fuzzy sets and prioritizes competing alternatives through the CoCoSo ranking process. Six evaluation criteria—cost, reliability, maintenance, environmental impact, supply stability, and policy support—were defined based on expert consultation. The proposed framework was applied to an industrial case study, demonstrating its capacity to manage conflicting objectives and deliver a transparent, rational ranking of energy alternatives. Sensitivity analysis confirmed the robustness of the results. The findings provide actionable insights for decision-makers and policymakers seeking data-driven strategies to enhance sustainable energy planning and operational efficiency. ]]> A Type-2 Fuzzy and CoCoSo-Based Strategic Decision Framework for Evaluating Energy Supply Alternatives mohamamd abdolshah doi: 10.56578/josa030403 Journal of Operational and Strategic Analytics 11-01-2025 Journal of Operational and Strategic Analytics 11-01-2025 2025 3 4 Article 237 10.56578/josa030403 https://www.acadlore.com/article/JOSA/2025_3_4/josa030403 https://www.acadlore.com/article/JOSA/2025_3_4/josa030402 As geopolitical competition intensifies and risks of global technological decoupling rise, the Digital Silk Road (DSR) is undergoing a strategic transition from the hard connectivity of physical infrastructure toward the soft connectivity of software ecological collaboration. Utilizing quarterly high-frequency indicators from the GitHub Innovation Graph (2020Q1–2025Q3), this study empirically examines the evolution of software collaboration networks between China and Belt and Road Initiative participating countries. We introduce the concept of digital gravity shift—a structural reorientation of innovation based on collaboration density and network resilience—to extend traditional innovation gravity theories. The findings reveal that: First, a significant digital gravity shift has occurred; unlike the stagnating Group of Seven (G7)-centric pathways, internal collaboration within the DSR exhibits a unique U-shaped resilience, where geopolitical shocks have paradoxically catalyzed the reorganization of innovation paths. Second, the collaboration model is transforming from unidirectional technology spillover toward bidirectional reciprocal symbiosis, signaling the maturation of digital social capital and mutual dependency within the Global South. Third, the substance of collaboration has achieved a qualitative leap from surface-level tasks to core system engineering, uncovering a leapfrog catch-up mechanism driven by the lower entry barriers of open-source modularity. This research provides granular empirical evidence for an emerging multipolar innovation landscape and offers strategic insights for mitigating technological fragmentation and enhancing national innovation resilience in the post-decoupling era. 10-20-2025 <![CDATA[ <p>As geopolitical competition intensifies and risks of global technological decoupling rise, the Digital Silk Road (DSR) is undergoing a strategic transition from the hard connectivity of physical infrastructure toward the soft connectivity of software ecological collaboration. Utilizing quarterly high-frequency indicators from the GitHub Innovation Graph (2020Q1–2025Q3), this study empirically examines the evolution of software collaboration networks between China and Belt and Road Initiative participating countries. We introduce the concept of digital gravity shift—a structural reorientation of innovation based on collaboration density and network resilience—to extend traditional innovation gravity theories. The findings reveal that: First, a significant digital gravity shift has occurred; unlike the stagnating Group of Seven (G7)-centric pathways, internal collaboration within the DSR exhibits a unique U-shaped resilience, where geopolitical shocks have paradoxically catalyzed the reorganization of innovation paths. Second, the collaboration model is transforming from unidirectional technology spillover toward bidirectional reciprocal symbiosis, signaling the maturation of digital social capital and mutual dependency within the Global South. Third, the substance of collaboration has achieved a qualitative leap from surface-level tasks to core system engineering, uncovering a leapfrog catch-up mechanism driven by the lower entry barriers of open-source modularity. This research provides granular empirical evidence for an emerging multipolar innovation landscape and offers strategic insights for mitigating technological fragmentation and enhancing national innovation resilience in the post-decoupling era.</p> ]]> Coding the Digital Silk Road: Evolution and Structural Transformation of Global Software Collaboration Networks lan huang minjing peng doi: 10.56578/josa030402 Journal of Operational and Strategic Analytics 10-20-2025 Journal of Operational and Strategic Analytics 10-20-2025 2025 3 4 Article 224 10.56578/josa030402 https://www.acadlore.com/article/JOSA/2025_3_4/josa030402 https://www.acadlore.com/article/JOSA/2025_3_4/josa030401 Enhancing logistics performance has been widely recognized as a critical pathway for accelerating economic development in emerging economies. In this context, a rigorous and objective assessment of national logistics performance remains essential. Accordingly, an integrated multi-criteria decision-making (MCDM) framework based on the Skewness Impact Through Distributional Evaluation (SITDE) method and the Multi-Attributive Border Approximation Area Comparison (MABAC) method was proposed for the systematic evaluation of logistics performance across the Emerging Seven (E7) economies. Within this framework, criterion weights were objectively derived using the SITDE method by capturing the distributional characteristics and skewness effects inherent in logistics performance indicators, thereby minimizing subjectivity in the weighting process. Subsequently, the MABAC method was employed to rank countries by quantifying their distances from criterion-specific boundary approximation areas. The empirical analysis focused on the E7 countries—China, India, Brazil, Russia, Indonesia, Mexico, and Türkiye—using the Logistics Performance Index (LPI) indicators obtained from the World Bank database. The results demonstrated that timeliness emerged as the most influential determinant of overall logistics performance. Among the E7 countries, China was identified as exhibiting the highest logistics performance, whereas Russia recorded the lowest performance level. Notably, Türkiye was ranked second, despite its comparatively lower level of economic development relative to several other E7 economies. The robustness and stability of the proposed SITDE–MABAC framework were further confirmed through comprehensive sensitivity and comparative analyses. Beyond methodological advancement, the findings offer important managerial, policy-oriented, and region-specific insights, providing evidence-based guidance for policymakers and logistics practitioners seeking to enhance logistics efficiency, resilience, and international competitiveness in developing economies. 09-30-2025 <![CDATA[ Enhancing logistics performance has been widely recognized as a critical pathway for accelerating economic development in emerging economies. In this context, a rigorous and objective assessment of national logistics performance remains essential. Accordingly, an integrated multi-criteria decision-making (MCDM) framework based on the Skewness Impact Through Distributional Evaluation (SITDE) method and the Multi-Attributive Border Approximation Area Comparison (MABAC) method was proposed for the systematic evaluation of logistics performance across the Emerging Seven (E7) economies. Within this framework, criterion weights were objectively derived using the SITDE method by capturing the distributional characteristics and skewness effects inherent in logistics performance indicators, thereby minimizing subjectivity in the weighting process. Subsequently, the MABAC method was employed to rank countries by quantifying their distances from criterion-specific boundary approximation areas. The empirical analysis focused on the E7 countries—China, India, Brazil, Russia, Indonesia, Mexico, and Türkiye—using the Logistics Performance Index (LPI) indicators obtained from the World Bank database. The results demonstrated that timeliness emerged as the most influential determinant of overall logistics performance. Among the E7 countries, China was identified as exhibiting the highest logistics performance, whereas Russia recorded the lowest performance level. Notably, Türkiye was ranked second, despite its comparatively lower level of economic development relative to several other E7 economies. The robustness and stability of the proposed SITDE–MABAC framework were further confirmed through comprehensive sensitivity and comparative analyses. Beyond methodological advancement, the findings offer important managerial, policy-oriented, and region-specific insights, providing evidence-based guidance for policymakers and logistics practitioners seeking to enhance logistics efficiency, resilience, and international competitiveness in developing economies. ]]> Measuring Logistics Performance in Emerging Economies: Insights from the SITDE–MABAC Method karahan kara galip cihan yalçın emre kadir özekenci doi: 10.56578/josa030401 Journal of Operational and Strategic Analytics 09-30-2025 Journal of Operational and Strategic Analytics 09-30-2025 2025 3 4 Article 211 10.56578/josa030401 https://www.acadlore.com/article/JOSA/2025_3_4/josa030401 https://www.acadlore.com/article/JOSA/2025_3_3/josa030305 Fuzzy data envelopment analysis (FDEA) plays an essential role in the current socio-economic scenario to analyze the performance of decision-making units (DMUs) within a fuzzy environment. This paper introduced a novel Bipolar Fuzzy Data Envelopment Analysis (BFDEA) model using bipolar triangular fuzzy numbers to accommodate both uncertainty and ambiguity in evaluating the performance of a finite number of DMUs. The BFDEA model utilizes a value function for bipolar fuzzy numbers and translates BFDEA models into equivalent crisp models, thus providing thorough and precise evaluations of efficiency. The BFDEA model embraces a super-efficiency framework to offer a full ranking of efficient DMUs, while establishing a benchmarking framework for a meaningful discussion of improvements in performance. A numerical example showed that the BFDEA method could provide a reliable nuanced evaluation even in the presence of conflicting information. This work contributes to the DEA literature, where uncertainty has been inadequately addressed up till the present, by providing breakthroughs in a convincing way for decision makers to analyze performance amidst complicated and indeterminate situations. 09-29-2025 <![CDATA[ Fuzzy data envelopment analysis (FDEA) plays an essential role in the current socio-economic scenario to analyze the performance of decision-making units (DMUs) within a fuzzy environment. This paper introduced a novel Bipolar Fuzzy Data Envelopment Analysis (BFDEA) model using bipolar triangular fuzzy numbers to accommodate both uncertainty and ambiguity in evaluating the performance of a finite number of DMUs. The BFDEA model utilizes a value function for bipolar fuzzy numbers and translates BFDEA models into equivalent crisp models, thus providing thorough and precise evaluations of efficiency. The BFDEA model embraces a super-efficiency framework to offer a full ranking of efficient DMUs, while establishing a benchmarking framework for a meaningful discussion of improvements in performance. A numerical example showed that the BFDEA method could provide a reliable nuanced evaluation even in the presence of conflicting information. This work contributes to the DEA literature, where uncertainty has been inadequately addressed up till the present, by providing breakthroughs in a convincing way for decision makers to analyze performance amidst complicated and indeterminate situations. ]]> Leveraging the Bipolar Fuzzy Numbers in Data Envelopment Analysis to Enhance the Performance Evaluation kshitish kumar mohanta doi: 10.56578/josa030305 Journal of Operational and Strategic Analytics 09-29-2025 Journal of Operational and Strategic Analytics 09-29-2025 2025 3 3 Article 199 10.56578/josa030305 https://www.acadlore.com/article/JOSA/2025_3_3/josa030305 https://www.acadlore.com/article/JOSA/2025_3_3/josa030304 Recently, the food industry has faced numerous challenges such as rising demand, climate change, and the imperative to improve the quality and safety of food products. This research investigated the role of Artificial intelligence (AI) and the Internet of Things (IoT) in managing food supply and distribution projects. The main objective of this study was to analyze how these technologies could be implemented to optimize the process of supply chain and enhance the efficiency and effectiveness in food distribution. Successful cases of technological implementation in the food industry highlighted the associated benefits and challenges of adopting AI and IoT. Ten critical factors influencing the roles of AI and IoT in food supply and distribution were identified and considered in the current study. Following a systematic coding process through meta-synthesis, concepts related to each factor were extracted from previous studies. Finally, expert opinions were gathered by a questionnaire survey whereas the Kappa index was calculated using SPSS software. The obtained value of 0.78 indicated a desirable agreement in the perspectives between researchers and experts. By leveraging AI, organizations are able to analyze big data, predict demand, optimize inventory, and reduce resource waste. Likewise, IoT, through connecting devices and sensors to the network, enables the collection of real-time data, which assists managers in making better decisions regarding the timing and location of food distribution. 09-24-2025 <![CDATA[ <p>Recently, the food industry has faced numerous challenges such as rising demand, climate change, and the imperative to improve the quality and safety of food products. This research investigated the role of Artificial intelligence (AI) and the Internet of Things (IoT) in managing food supply and distribution projects. The main objective of this study was to analyze how these technologies could be implemented to optimize the process of supply chain and enhance the efficiency and effectiveness in food distribution. Successful cases of technological implementation in the food industry highlighted the associated benefits and challenges of adopting AI and IoT. Ten critical factors influencing the roles of AI and IoT in food supply and distribution were identified and considered in the current study. Following a systematic coding process through meta-synthesis, concepts related to each factor were extracted from previous studies. Finally, expert opinions were gathered by a questionnaire survey whereas the Kappa index was calculated using SPSS software. The obtained value of 0.78 indicated a desirable agreement in the perspectives between researchers and experts. By leveraging AI, organizations are able to analyze big data, predict demand, optimize inventory, and reduce resource waste. Likewise, IoT, through connecting devices and sensors to the network, enables the collection of real-time data, which assists managers in making better decisions regarding the timing and location of food distribution.</p> ]]> Roles of Artificial Intelligence and the Internet of Things (IoT) in the Project Management of Food Supply and Distribution mona khodadadi doi: 10.56578/josa030304 Journal of Operational and Strategic Analytics 09-24-2025 Journal of Operational and Strategic Analytics 09-24-2025 2025 3 3 Article 192 10.56578/josa030304 https://www.acadlore.com/article/JOSA/2025_3_3/josa030304 https://www.acadlore.com/article/JOSA/2025_3_3/josa030303 Applying the Theory of Planned Behavior (TPB), this study provided an enhanced understanding of the intentions, motivations, and beliefs about blood donation among the young generation in the U.S. An online quantitative Qualtrics survey was administered at a large public university to collect data from the campus community, with participants aged 18 to 39 (N = 954). Data were collected via an adapted questionnaire on the TPB constructs: attitudes towards blood donation, subjective norms of peers and loved ones, perceived control of behavior, and intention to donate blood. Univariate, bivariate and multivariate analysis were employed to explore the associations of these constructs. Primary findings revealed that the intention to donate blood regularly was positively associated with social norms. Secondary findings suggested that a hierarchical multiple regression analysis provided strong support for the role of social media apps as a major determinant of motivations for donating blood, with TPB constructs accounting for 34% of the variance. Tertiary findings from this study derived Cronbach’s $\alpha$ = 0.555, indicating a poor level of internal consistency. The generalizability of the results in this study could be verified by increasing the number of questions in each construct and conducting future studies at larger universities and blood centers. 09-22-2025 <![CDATA[ <p>Applying the Theory of Planned Behavior (TPB), this study provided an enhanced understanding of the intentions, motivations, and beliefs about blood donation among the young generation in the U.S. An online quantitative Qualtrics survey was administered at a large public university to collect data from the campus community, with participants aged 18 to 39 (N = 954). Data were collected via an adapted questionnaire on the TPB constructs: attitudes towards blood donation, subjective norms of peers and loved ones, perceived control of behavior, and intention to donate blood. Univariate, bivariate and multivariate analysis were employed to explore the associations of these constructs. Primary findings revealed that the intention to donate blood regularly was positively associated with social norms. Secondary findings suggested that a hierarchical multiple regression analysis provided strong support for the role of social media apps as a major determinant of motivations for donating blood, with TPB constructs accounting for 34% of the variance. Tertiary findings from this study derived Cronbach’s $\alpha$ = 0.555, indicating a poor level of internal consistency. The generalizability of the results in this study could be verified by increasing the number of questions in each construct and conducting future studies at larger universities and blood centers.</p> ]]> Intentions, Motivations, and Beliefs about Blood Donation: A Pilot Study at a Large Public University afekwo mary ukuku robert s. keyser lin li maria valero brooke berman omajadesola bamidele zahra sobhani doi: 10.56578/josa030303 Journal of Operational and Strategic Analytics 09-22-2025 Journal of Operational and Strategic Analytics 09-22-2025 2025 3 3 Article 182 10.56578/josa030303 https://www.acadlore.com/article/JOSA/2025_3_3/josa030303 https://www.acadlore.com/article/JOSA/2025_3_3/josa030302 Managing the public sector increasingly requires the application of modern analytical methods that enable decision-making based on multiple criteria. This paper presents a real-world case study in which multicriteria decision-making (MCDM) method sare applied to evaluate the marketing activities and performance of a public institution. The research includes an analysis of the services offered, user satisfaction, and a comparison with alternative institutions in the same field. The obtained results highlight the relevance of MCDM methods for the objective assessment of public services and for strategic planning within the public sector. The paper contributes to a better understanding of the potential for applying MCDM tools in the context of public administration, with particular emphasis on marketing as a mechanism for improving transparency and effectiveness. 09-09-2025 <![CDATA[ Managing the public sector increasingly requires the application of modern analytical methods that enable decision-making based on multiple criteria. This paper presents a real-world case study in which multicriteria decision-making (MCDM) method sare applied to evaluate the marketing activities and performance of a public institution. The research includes an analysis of the services offered, user satisfaction, and a comparison with alternative institutions in the same field. The obtained results highlight the relevance of MCDM methods for the objective assessment of public services and for strategic planning within the public sector. The paper contributes to a better understanding of the potential for applying MCDM tools in the context of public administration, with particular emphasis on marketing as a mechanism for improving transparency and effectiveness. ]]> Multicriteria Decision-Making in the Evaluation of Public Services: Application of MCDM Methods in a Real Case Study milica stanković maja ivanović ðukić aleksandar stanković suzana stefanović doi: 10.56578/josa030302 Journal of Operational and Strategic Analytics 09-09-2025 Journal of Operational and Strategic Analytics 09-09-2025 2025 3 3 Article 172 10.56578/josa030302 https://www.acadlore.com/article/JOSA/2025_3_3/josa030302 https://www.acadlore.com/article/JOSA/2025_3_3/josa030301 Imperfect production and rework in contemporary manufacturing systems, are inevitable realities hampering overall performance and cost efficiency. To address this challenge, this study developed an Economic Production Quantity (EPQ) model which integrated defective items, rework, disposal, and penalties for lost sales within a fuzzy decision-making framework. The convexity of the model implied the possible existence of an optimal solution. Compared to conventional crisp models, the proposed approach provided a more robust and realistic evaluation of inventory and cost structures by representing indeterminate parameters such as production cost, backordering cost, and penalty cost through Hexagonal Fuzzy Numbers (HFNs) and Graded Mean Deviation Method (GMDM) for defuzzification. The numerical illustration demonstrated superiority of the fuzzy model in minimizing the total cost, balancing inventory levels, and enhancing service quality. Sensitivity analysis further highlighted the adaptability of the model to combat unpredictable changes in the parameters. The study concluded with valuable insights for decision-makers to optimize imperfect production processes, strengthen resource allocation, and tackle uncertainty in real-world manufacturing environment. 08-20-2025 <![CDATA[ Imperfect production and rework in contemporary manufacturing systems, are inevitable realities hampering overall performance and cost efficiency. To address this challenge, this study developed an Economic Production Quantity (EPQ) model which integrated defective items, rework, disposal, and penalties for lost sales within a fuzzy decision-making framework. The convexity of the model implied the possible existence of an optimal solution. Compared to conventional crisp models, the proposed approach provided a more robust and realistic evaluation of inventory and cost structures by representing indeterminate parameters such as production cost, backordering cost, and penalty cost through Hexagonal Fuzzy Numbers (HFNs) and Graded Mean Deviation Method (GMDM) for defuzzification. The numerical illustration demonstrated superiority of the fuzzy model in minimizing the total cost, balancing inventory levels, and enhancing service quality. Sensitivity analysis further highlighted the adaptability of the model to combat unpredictable changes in the parameters. The study concluded with valuable insights for decision-makers to optimize imperfect production processes, strengthen resource allocation, and tackle uncertainty in real-world manufacturing environment. ]]> Hexagonal Fuzzy-Based Review on Imperfect EPQ Models Involving Rework and Lost Sales Penalties kuppulakshmi vadivel sugapriya chandrasekar nagarajan deivanayagampillai doi: 10.56578/josa030301 Journal of Operational and Strategic Analytics 08-20-2025 Journal of Operational and Strategic Analytics 08-20-2025 2025 3 3 Article 162 10.56578/josa030301 https://www.acadlore.com/article/JOSA/2025_3_3/josa030301 https://www.acadlore.com/article/JOSA/2025_3_2/josa030205 The COVID-19 pandemic significantly challenged business resilience, particularly in the healthcare sector, where pharmaceutical and biotechnology companies experienced growth and service-oriented entities faced operational stress. In this study, the advanced Multi-Criteria Decision-Making (MCDM) techniques were employed to investigate the financial performance of healthcare firms listed on the Standard and Poor's 500 index from year 2018 to 2023. The research evaluated ten firms based on 16 criteria, encompassing both financial and non-financial dimensions. The financial criteria included Leverage Ratio, Tobin's Q Ratio, Revenue Growth, Operating Profit Growth, Equity Growth, Firm Size, Net Income, Total Liabilities, Revenue, Operating Profit, and Market Capitalization. In parallel, the non-financial indicators such as Human Resource Management, Supply Chain Management, Risk and Crisis Management, Business Ethics, and Environmental Policy were integrated to reflect managerial quality and sustainability practices. Out of the 16 criteria, two costs and nine benefits were quantitative whereas the remaining five benefits were qualitative. Expert assessments were modeled on the Spherical Cubic Fuzzy (SCF) sets and aggregated with the Aczel–Alsina operator. Alternatives were ranked using methods like the Ranking of Alternatives through Nested Cumulative Operator Method (RANCOM) and the Alternative Ranking Order Method with Adjustment Normalization (AROMAN), hence producing a multidimensional evaluation matrix enriched by both numerical and verbal judgments from ten experts. This research contributed to the literature in three key ways: (1) It provided a holistic assessment of financial performance in a highly dynamic and uncertain environment; (2) It broadened the performance evaluation framework to include non-financial and sustainability-driven criteria; and (3) It demonstrated the utility of novel MCDM tools like the SCF sets, the Aczel–Alsina aggregation, the RANCOM, and the AROMAN in complicated decision environments. The study offers a robust and innovative analytical model for academics and practitioners seeking to understand firm resilience and performance amid crises. 06-29-2025 <![CDATA[ The COVID-19 pandemic significantly challenged business resilience, particularly in the healthcare sector, where pharmaceutical and biotechnology companies experienced growth and service-oriented entities faced operational stress. In this study, the advanced Multi-Criteria Decision-Making (MCDM) techniques were employed to investigate the financial performance of healthcare firms listed on the Standard and Poor's 500 index from year 2018 to 2023. The research evaluated ten firms based on 16 criteria, encompassing both financial and non-financial dimensions. The financial criteria included Leverage Ratio, Tobin's Q Ratio, Revenue Growth, Operating Profit Growth, Equity Growth, Firm Size, Net Income, Total Liabilities, Revenue, Operating Profit, and Market Capitalization. In parallel, the non-financial indicators such as Human Resource Management, Supply Chain Management, Risk and Crisis Management, Business Ethics, and Environmental Policy were integrated to reflect managerial quality and sustainability practices. Out of the 16 criteria, two costs and nine benefits were quantitative whereas the remaining five benefits were qualitative. Expert assessments were modeled on the Spherical Cubic Fuzzy (SCF) sets and aggregated with the Aczel–Alsina operator. Alternatives were ranked using methods like the Ranking of Alternatives through Nested Cumulative Operator Method (RANCOM) and the Alternative Ranking Order Method with Adjustment Normalization (AROMAN), hence producing a multidimensional evaluation matrix enriched by both numerical and verbal judgments from ten experts. This research contributed to the literature in three key ways: (1) It provided a holistic assessment of financial performance in a highly dynamic and uncertain environment; (2) It broadened the performance evaluation framework to include non-financial and sustainability-driven criteria; and (3) It demonstrated the utility of novel MCDM tools like the SCF sets, the Aczel–Alsina aggregation, the RANCOM, and the AROMAN in complicated decision environments. The study offers a robust and innovative analytical model for academics and practitioners seeking to understand firm resilience and performance amid crises. ]]> Performance Evaluation of Healthcare Companies with Hybrid Multi-Criteria Decision-Making (MCDM) Methods During the COVID-19 Pandemic hamide özyürek galip cihan yalçın karahan kara mustafa polat gökhan şahin doi: 10.56578/josa030205 Journal of Operational and Strategic Analytics 06-29-2025 Journal of Operational and Strategic Analytics 06-29-2025 2025 3 2 Article 134 10.56578/josa030205 https://www.acadlore.com/article/JOSA/2025_3_2/josa030205 https://www.acadlore.com/article/JOSA/2025_3_2/josa030204 The accurate assessment of contract renewal risk at the individual policyholder level represents a critical component of risk management in non-life insurance and is essential for ensuring long-term business sustainability. In this study, a two-stage interval type-2 fuzzy decision-making framework was proposed to evaluate and classify policyholder renewal risk. The approach began with the identification of key risk factors (RFs) that exert the most significant influence on renewal outcomes and overall business risk. The relative importance of these RFs was expressed through predefined linguistic terms, which were systematically mapped to interval type-2 triangular fuzzy numbers (IT2TFNs). The Fuzzy Best-Worst Method (FBWM) was applied to derive the optimal weight vector of RFs. Subsequently, the values of the identified RFs were quantified based on available operational and historical insurance data. Using type-2 fuzzy algebra, a weighted normalized decision matrix was constructed. In the second stage, a novel Pareto analysis extended with interval type-2 fuzzy numbers (IT2FNs) was introduced to classify policyholders according to their associated renewal risk levels. This integration enabled the simultaneous consideration of both factor weights and their fuzzy performance values, ensuring that high-risk policyholders are effectively distinguished from lower-risk groups. The proposed framework was validated through a real-world case study in the non-life insurance sector. By integrating the strengths of FBWM and fuzzy Pareto analysis, the framework provides an original and rigorous methodology for risk assessment in non-life insurance, contributing to both academic research and practical applications in the domain of sustainable insurance management. 06-29-2025 <![CDATA[ The accurate assessment of contract renewal risk at the individual policyholder level represents a critical component of risk management in non-life insurance and is essential for ensuring long-term business sustainability. In this study, a two-stage interval type-2 fuzzy decision-making framework was proposed to evaluate and classify policyholder renewal risk. The approach began with the identification of key risk factors (RFs) that exert the most significant influence on renewal outcomes and overall business risk. The relative importance of these RFs was expressed through predefined linguistic terms, which were systematically mapped to interval type-2 triangular fuzzy numbers (IT2TFNs). The Fuzzy Best-Worst Method (FBWM) was applied to derive the optimal weight vector of RFs. Subsequently, the values of the identified RFs were quantified based on available operational and historical insurance data. Using type-2 fuzzy algebra, a weighted normalized decision matrix was constructed. In the second stage, a novel Pareto analysis extended with interval type-2 fuzzy numbers (IT2FNs) was introduced to classify policyholders according to their associated renewal risk levels. This integration enabled the simultaneous consideration of both factor weights and their fuzzy performance values, ensuring that high-risk policyholders are effectively distinguished from lower-risk groups. The proposed framework was validated through a real-world case study in the non-life insurance sector. By integrating the strengths of FBWM and fuzzy Pareto analysis, the framework provides an original and rigorous methodology for risk assessment in non-life insurance, contributing to both academic research and practical applications in the domain of sustainable insurance management. ]]> A Two-Stage Interval Type-2 Fuzzy Approach for Contract Renewal Risk Assessment in Non-Life Insurance Using BWM and Pareto Analysis mirjana misita predrag mimović danijela tadić doi: 10.56578/josa030204 Journal of Operational and Strategic Analytics 06-29-2025 Journal of Operational and Strategic Analytics 06-29-2025 2025 3 2 Article 120 10.56578/josa030204 https://www.acadlore.com/article/JOSA/2025_3_2/josa030204 https://www.acadlore.com/article/JOSA/2025_3_2/josa030203 This work builds on hypergraphs—graphs whose edges can link any number of vertices—and superhypergraphs, which add a recursive, hierarchical powerset structure to hyperedges. It reviews four practical hypergraph variants: Knowledge Hypergraphs (for multi‐relational knowledge representation), Multimodal Hypergraphs (for combining different data modalities), Lattice Hypergraphs (for spatial and topological modeling), and Hyperbolic Hypergraphs (for embedding vertices in hyperbolic space to capture hierarchies). The paper then shows how to elevate each of these into the superhypergraph framework—resulting in Knowledge SuperHypergraphs, Multimodal SuperHypergraphs, Lattice SuperHypergraphs, and Hyperbolic SuperHypergraphs—and outlines their core properties. Overall, it offers a unified, more expressive modeling approach that paves the way for future advances in both hypergraph and superhypergraph research. 06-29-2025 <![CDATA[ This work builds on hypergraphs—graphs whose edges can link any number of vertices—and superhypergraphs, which add a recursive, hierarchical powerset structure to hyperedges. It reviews four practical hypergraph variants: Knowledge Hypergraphs (for multi‐relational knowledge representation), Multimodal Hypergraphs (for combining different data modalities), Lattice Hypergraphs (for spatial and topological modeling), and Hyperbolic Hypergraphs (for embedding vertices in hyperbolic space to capture hierarchies). The paper then shows how to elevate each of these into the superhypergraph framework—resulting in Knowledge SuperHypergraphs, Multimodal SuperHypergraphs, Lattice SuperHypergraphs, and Hyperbolic SuperHypergraphs—and outlines their core properties. Overall, it offers a unified, more expressive modeling approach that paves the way for future advances in both hypergraph and superhypergraph research. ]]> Knowledge SuperHypergraphs, Multimodal SuperHypergraphs, Lattice SuperHypergraphs, and Hyperbolic SuperHypergraphs: Concepts and Applications takaaki fujita doi: 10.56578/josa030203 Journal of Operational and Strategic Analytics 06-29-2025 Journal of Operational and Strategic Analytics 06-29-2025 2025 3 2 Article 95 10.56578/josa030203 https://www.acadlore.com/article/JOSA/2025_3_2/josa030203 https://www.acadlore.com/article/JOSA/2025_3_2/josa030202 As the most widely played and commercially influential sport worldwide, football (soccer) demands increasingly data-driven and methodologically sound decision-making across tactical, operational, and financial domains. In recent years, Multi-Criteria Decision-Making (MCDM) methods have been increasingly adopted to address the complex, multi-dimensional challenges faced by stakeholders in the sport. To comprehensively examine the current state of research, a systematic literature review (SLR) was conducted focusing on the application of MCDM techniques in football-related decision contexts. The analysis was performed using articles indexed in the Scopus and Web of Science databases, with the Novelty, Impact, Relevance, and Prestige (NIRP) method employed to filter and prioritize the most impactful publications. A final portfolio of 27 articles published between 2000 and 2024 was identified and examined. The selected works were analyzed to identify prevailing MCDM techniques, thematic concentrations, and methodological trends within the domain, providing a comprehensive overview of developments in this field. This review is expected to serve as a foundational reference for academics and practitioners seeking to leverage decision-making frameworks in the evolving landscape of football analytics. 05-15-2025 <![CDATA[ <p>As the most widely played and commercially influential sport worldwide, football (soccer) demands increasingly data-driven and methodologically sound decision-making across tactical, operational, and financial domains. In recent years, Multi-Criteria Decision-Making (MCDM) methods have been increasingly adopted to address the complex, multi-dimensional challenges faced by stakeholders in the sport. To comprehensively examine the current state of research, a systematic literature review (SLR) was conducted focusing on the application of MCDM techniques in football-related decision contexts. The analysis was performed using articles indexed in the Scopus and Web of Science databases, with the Novelty, Impact, Relevance, and Prestige (NIRP) method employed to filter and prioritize the most impactful publications. A final portfolio of 27 articles published between 2000 and 2024 was identified and examined. The selected works were analyzed to identify prevailing MCDM techniques, thematic concentrations, and methodological trends within the domain, providing a comprehensive overview of developments in this field. This review is expected to serve as a foundational reference for academics and practitioners seeking to leverage decision-making frameworks in the evolving landscape of football analytics.</p> ]]> MCDM and Soccer: A Systematic Review of Key Aspects, Trends, and Future Perspectives maiquiel schmidt de oliveira vilmar steffen doi: 10.56578/josa030202 Journal of Operational and Strategic Analytics 05-15-2025 Journal of Operational and Strategic Analytics 05-15-2025 2025 3 2 Article 80 10.56578/josa030202 https://www.acadlore.com/article/JOSA/2025_3_2/josa030202 https://www.acadlore.com/article/JOSA/2025_3_2/josa030201 The corporate financial performance of Turkish insurance companies was evaluated through the development of a novel hybrid multi-criteria decision-making (MCDM) framework, integrating the Ranking Comparison (RANCOM), Simple Weight Calculation (SIWEC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) methodologies. Within this framework, financial indicators were selected based on expert input, and indicator weights were determined through the combined application of RANCOM and SIWEC methods. Subsequently, company rankings were established by employing the MAIRCA method. To ensure the robustness and reliability of the proposed framework, extensive sensitivity analyses were conducted. The findings identified the current ratio, defined as the ratio of current assets to current liabilities, as a critical determinant of financial performance. Türkiye Sigorta was consistently ranked as the top-performing company over the analyzed period. The outcomes of the sensitivity analyses confirmed the stability and effectiveness of the proposed decision-making model in assessing corporate financial performance within the insurance industry. This study contributes to the financial performance evaluation literature by demonstrating the applicability and advantages of hybrid MCDM approaches in dynamic and highly regulated sectors such as insurance. 05-07-2025 <![CDATA[ The corporate financial performance of Turkish insurance companies was evaluated through the development of a novel hybrid multi-criteria decision-making (MCDM) framework, integrating the Ranking Comparison (RANCOM), Simple Weight Calculation (SIWEC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) methodologies. Within this framework, financial indicators were selected based on expert input, and indicator weights were determined through the combined application of RANCOM and SIWEC methods. Subsequently, company rankings were established by employing the MAIRCA method. To ensure the robustness and reliability of the proposed framework, extensive sensitivity analyses were conducted. The findings identified the current ratio, defined as the ratio of current assets to current liabilities, as a critical determinant of financial performance. Türkiye Sigorta was consistently ranked as the top-performing company over the analyzed period. The outcomes of the sensitivity analyses confirmed the stability and effectiveness of the proposed decision-making model in assessing corporate financial performance within the insurance industry. This study contributes to the financial performance evaluation literature by demonstrating the applicability and advantages of hybrid MCDM approaches in dynamic and highly regulated sectors such as insurance. ]]> A Novel MCDM Framework for Evaluating Corporate Financial Performance: Evidence from the Turkish Insurance Sector yusuf akgül doi: 10.56578/josa030201 Journal of Operational and Strategic Analytics 05-07-2025 Journal of Operational and Strategic Analytics 05-07-2025 2025 3 2 Article 65 10.56578/josa030201 https://www.acadlore.com/article/JOSA/2025_3_2/josa030201 https://www.acadlore.com/article/JOSA/2025_3_1/josa030105 A novel integrated Multi-Criteria Decision-Making (MCDM) framework was proposed to address the complex challenge of assessing renewable energy performance. The framework incorporates the Modified Standard Deviation (MSD) method and the Criteria Importance Through Intercriteria Correlation (CRITIC) approach to objectively determine the weights of performance indicators, while the Ranking of Alternatives by the Weights of the Criteria (RAWEC) method was applied to derive annual performance rankings. A real-time case study covering Turkey over the period 2015–2023 was conducted to validate the proposed model. A total of ten criteria were identified to comprehensively evaluate the renewable energy performance of Turkey. The empirical findings revealed that the average annual growth rate of installed renewable power capacity, the share of electricity generated from renewables in total electricity generation, and the absolute quantity of electricity produced from renewable sources exerted the greatest influence on performance outcomes. According to the RAWEC-based ranking, the year 2023 emerged as the most successful in terms of renewable energy advancement during the observed period. These findings provide critical insights for policymakers and stakeholders, supporting evidence-based decision-making for enhancing energy security, achieving environmental sustainability, and guiding national energy strategy. The proposed integrated framework demonstrates a robust, data-driven approach that may be adapted to other national contexts or timeframes to support the monitoring, evaluation, and strategic planning of renewable energy systems. Ultimately, the study contributes to the broader discourse on sustainable development and climate change mitigation by offering a replicable and scalable assessment methodology. 03-30-2025 <![CDATA[ A novel integrated Multi-Criteria Decision-Making (MCDM) framework was proposed to address the complex challenge of assessing renewable energy performance. The framework incorporates the Modified Standard Deviation (MSD) method and the Criteria Importance Through Intercriteria Correlation (CRITIC) approach to objectively determine the weights of performance indicators, while the Ranking of Alternatives by the Weights of the Criteria (RAWEC) method was applied to derive annual performance rankings. A real-time case study covering Turkey over the period 2015–2023 was conducted to validate the proposed model. A total of ten criteria were identified to comprehensively evaluate the renewable energy performance of Turkey. The empirical findings revealed that the average annual growth rate of installed renewable power capacity, the share of electricity generated from renewables in total electricity generation, and the absolute quantity of electricity produced from renewable sources exerted the greatest influence on performance outcomes. According to the RAWEC-based ranking, the year 2023 emerged as the most successful in terms of renewable energy advancement during the observed period. These findings provide critical insights for policymakers and stakeholders, supporting evidence-based decision-making for enhancing energy security, achieving environmental sustainability, and guiding national energy strategy. The proposed integrated framework demonstrates a robust, data-driven approach that may be adapted to other national contexts or timeframes to support the monitoring, evaluation, and strategic planning of renewable energy systems. Ultimately, the study contributes to the broader discourse on sustainable development and climate change mitigation by offering a replicable and scalable assessment methodology. ]]> Assessment of Renewable Energy Performance in Turkey Using a Novel Integrated MSD-CRITIC-RAWEC Model zeynep durmuș doi: 10.56578/josa030105 Journal of Operational and Strategic Analytics 03-30-2025 Journal of Operational and Strategic Analytics 03-30-2025 2025 3 1 Article 49 10.56578/josa030105 https://www.acadlore.com/article/JOSA/2025_3_1/josa030105 https://www.acadlore.com/article/JOSA/2025_3_1/josa030104 Supply chain digitalization (SCD) has been recognized as a critical enabler of high-quality development in the manufacturing sector. To explore its influence mechanisms, an SCD indicator was constructed through textual analysis of corporate disclosures by Chinese manufacturing firms listed on the Shanghai and Shenzhen A-share markets from 2008 to 2022. Based on the theoretical lens of supply chain integration, the impact of SCD on high-quality development was empirically examined. The findings indicate that SCD significantly promotes high-quality development across manufacturing firms. Further analysis revealed that this relationship is positively mediated by two core mechanisms: supply chain collaborative innovation and the advancement of supply chain finance (SCF). These mediating effects were found to be strengthened under conditions of heightened environmental dynamism, underscoring the adaptive value of digital supply chain capabilities in volatile contexts. Heterogeneity analysis demonstrated that the positive effects of SCD are more pronounced in non-state-owned enterprises, firms in growth or decline stages, and those characterized by low levels of resource slack. Additionally, the long-term economic consequences of SCD were evaluated, and it was observed that enhanced digitalization contributes to the stable growth of firms’ long-term value by reinforcing their high-quality development trajectories. By clarifying the pathways through which SCD influences development outcomes, this study offers empirical evidence that enriches the existing body of literature on digital transformation within supply chains. Moreover, practical implications are provided for policy formulation and strategic decision-making aimed at fostering digitally integrated, innovation-driven, and financially resilient manufacturing ecosystems. 03-30-2025 <![CDATA[ Supply chain digitalization (SCD) has been recognized as a critical enabler of high-quality development in the manufacturing sector. To explore its influence mechanisms, an SCD indicator was constructed through textual analysis of corporate disclosures by Chinese manufacturing firms listed on the Shanghai and Shenzhen A-share markets from 2008 to 2022. Based on the theoretical lens of supply chain integration, the impact of SCD on high-quality development was empirically examined. The findings indicate that SCD significantly promotes high-quality development across manufacturing firms. Further analysis revealed that this relationship is positively mediated by two core mechanisms: supply chain collaborative innovation and the advancement of supply chain finance (SCF). These mediating effects were found to be strengthened under conditions of heightened environmental dynamism, underscoring the adaptive value of digital supply chain capabilities in volatile contexts. Heterogeneity analysis demonstrated that the positive effects of SCD are more pronounced in non-state-owned enterprises, firms in growth or decline stages, and those characterized by low levels of resource slack. Additionally, the long-term economic consequences of SCD were evaluated, and it was observed that enhanced digitalization contributes to the stable growth of firms’ long-term value by reinforcing their high-quality development trajectories. By clarifying the pathways through which SCD influences development outcomes, this study offers empirical evidence that enriches the existing body of literature on digital transformation within supply chains. Moreover, practical implications are provided for policy formulation and strategic decision-making aimed at fostering digitally integrated, innovation-driven, and financially resilient manufacturing ecosystems. ]]> Mechanisms of Supply Chain Digitalization in Advancing High-Quality Development in Manufacturing Firms haohua liu zhongfa yu doi: 10.56578/josa030104 Journal of Operational and Strategic Analytics 03-30-2025 Journal of Operational and Strategic Analytics 03-30-2025 2025 3 1 Article 34 10.56578/josa030104 https://www.acadlore.com/article/JOSA/2025_3_1/josa030104 https://www.acadlore.com/article/JOSA/2025_3_1/josa030103 Ensuring the integrity of goods during cold chain transportation remains a critical challenge in logistics, as it is essential to preserve product quality, freshness, and compliance with stringent safety standards. Strategic decision-making in this context requires the prioritization of customer requirements and the optimal allocation of limited operational resources. In response to these demands, an integrated Multi-Criteria Decision-Making (MCDM) model was developed by combining the Best-Worst Method (BWM), Quality Function Deployment (QFD), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) approach. Within this framework, BWM was utilized to determine the relative importance of user requirements, which were then mapped onto specific operational resources through QFD to identify critical resource elements and derive their corresponding weights. These weights, subsequently treated as evaluation criteria in the MARCOS method, were applied to assess the performance of Third-Party Logistics (3PL) providers. The proposed methodology was validated through a case study involving eight user requirements and seven key resources. The findings indicated that precise temperature control and delivery speed were the most critical user requirements, whereas advanced temperature sensors and vehicles with cooling systems were identified as the most significant resources. Based on the MARCOS evaluation, Provider 1 emerged as the most optimal 3PL alternative. This integrated decision-making model offers a systematic and data-driven approach for aligning customer priorities with resource capabilities, thereby enabling logistics providers to enhance service quality, operational efficiency, and strategic competitiveness in temperature-sensitive supply chains. The model also demonstrates practical scalability and adaptability across diverse cold chain scenarios. 03-30-2025 <![CDATA[ <p><span style="font-family: Times New Roman, serif">Ensuring the integrity of goods during cold chain transportation remains a critical challenge in logistics, as it is essential to preserve product quality, freshness, and compliance with stringent safety standards. Strategic decision-making in this context requires the prioritization of customer requirements and the optimal allocation of limited operational resources. In response to these demands, an integrated Multi-Criteria Decision-Making (MCDM) model was developed by combining the Best-Worst Method (BWM), Quality Function Deployment (QFD), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) approach. Within this framework, BWM was utilized to determine the relative importance of user requirements, which were then mapped onto specific operational resources through QFD to identify critical resource elements and derive their corresponding weights. These weights, subsequently treated as evaluation criteria in the MARCOS method, were applied to assess the performance of Third-Party Logistics (3PL) providers. The proposed methodology was validated through a case study involving eight user requirements and seven key resources. The findings indicated that precise temperature control and delivery speed were the most critical user requirements, whereas advanced temperature sensors and vehicles with cooling systems were identified as the most significant resources. Based on the MARCOS evaluation, Provider 1 emerged as the most optimal 3PL alternative. This integrated decision-making model offers a systematic and data-driven approach for aligning customer priorities with resource capabilities, thereby enabling logistics providers to enhance service quality, operational efficiency, and strategic competitiveness in temperature-sensitive supply chains. The model also demonstrates practical scalability and adaptability across diverse cold chain scenarios.</span></p> ]]> Integrated BWM–QFD–MARCOS Framework for Strategic Decision-Making in Cold Chain Logistics milan andrejić vukašin pajić doi: 10.56578/josa030103 Journal of Operational and Strategic Analytics 03-30-2025 Journal of Operational and Strategic Analytics 03-30-2025 2025 3 1 Article 23 10.56578/josa030103 https://www.acadlore.com/article/JOSA/2025_3_1/josa030103 https://www.acadlore.com/article/JOSA/2025_3_1/josa030102 The enhancement of governance and the implementation of effective anti-corruption strategies are critical for fostering public trust, accountability, and transparency in developing countries. In this study, a structured approach was adopted to identify and prioritize key strategies for improving governance and combating corruption in Nigeria. An extensive literature review, supplemented by expert consultation, led to the identification of eight fundamental strategies. To systematically determine their relative significance, the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) method was employed. The findings indicate that strengthening the legal and regulatory framework through effective enforcement, judicial reforms, and the establishment of independent oversight bodies with legal protection and operational autonomy are the most impactful measures. These strategies are essential for enhancing public trust, accountability, and transparency in Nigeria. The insights derived from this study provide a robust foundation for policymakers and stakeholders seeking to implement targeted anti-corruption reforms in Nigeria and other developing economies facing similar governance challenges. 03-13-2025 <![CDATA[ The enhancement of governance and the implementation of effective anti-corruption strategies are critical for fostering public trust, accountability, and transparency in developing countries. In this study, a structured approach was adopted to identify and prioritize key strategies for improving governance and combating corruption in Nigeria. An extensive literature review, supplemented by expert consultation, led to the identification of eight fundamental strategies. To systematically determine their relative significance, the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) method was employed. The findings indicate that strengthening the legal and regulatory framework through effective enforcement, judicial reforms, and the establishment of independent oversight bodies with legal protection and operational autonomy are the most impactful measures. These strategies are essential for enhancing public trust, accountability, and transparency in Nigeria. The insights derived from this study provide a robust foundation for policymakers and stakeholders seeking to implement targeted anti-corruption reforms in Nigeria and other developing economies facing similar governance challenges. ]]> Prioritizing Governance and Anti-Corruption Strategies in Nigeria Using the Fermatean Fuzzy Method mouhamed bayane bouraima machioud maxime sangaré oumar doi: 10.56578/josa030102 Journal of Operational and Strategic Analytics 03-13-2025 Journal of Operational and Strategic Analytics 03-13-2025 2025 3 1 Article 14 10.56578/josa030102 https://www.acadlore.com/article/JOSA/2025_3_1/josa030102 https://www.acadlore.com/article/JOSA/2025_3_1/josa030101 Linear systems often involve coefficients that are uncertain or imprecise due to inherent variability and vagueness in the data. In scenarios where only approximate or vague knowledge of the system parameters is available, traditional fuzzy logic is commonly employed. However, conventional fuzzy logic may be inadequate when defining a membership degree with a single, precise value proves difficult. In such cases, Single-Valued Trapezoidal Neutrosophic Numbers (SVTrNNs) offer a more suitable framework, as they account for indeterminacy, alongside truth and falsity. The solution of Single-Valued Trapezoidal Neutrosophic Linear Equations (SVTrNLEs) was explored in this study using an embedding approach. The approach reformulates the SVTrNLEs into an equivalent crisp linear system, enabling the application of conventional solution methods. The solution was then obtained using either the matrix inversion method or the gradient descent optimization algorithm implemented in PyTorch. The robustness and adaptability of gradient-based optimization techniques were thoroughly assessed. The learning process minimizes the residual error iteratively, with convergence behaviour and numerical stability analyzed across various parameter configurations. The results demonstrate rapid convergence, proximity to exact solutions, and significant robustness to parameter variability, highlighting the efficacy of gradient descent for solving uncertain linear systems. These findings provide a foundation for the extension of gradient-based methods to more complex systems and broader applications. Furthermore, the existence and uniqueness of the neutrosophic solution to an $n\times n$ linear system were rigorously analyzed, with numerical examples provided to assess the reliability and efficiency of the proposed methods. 03-02-2025 <![CDATA[ Linear systems often involve coefficients that are uncertain or imprecise due to inherent variability and vagueness in the data. In scenarios where only approximate or vague knowledge of the system parameters is available, traditional fuzzy logic is commonly employed. However, conventional fuzzy logic may be inadequate when defining a membership degree with a single, precise value proves difficult. In such cases, Single-Valued Trapezoidal Neutrosophic Numbers (SVTrNNs) offer a more suitable framework, as they account for indeterminacy, alongside truth and falsity. The solution of Single-Valued Trapezoidal Neutrosophic Linear Equations (SVTrNLEs) was explored in this study using an embedding approach. The approach reformulates the SVTrNLEs into an equivalent crisp linear system, enabling the application of conventional solution methods. The solution was then obtained using either the matrix inversion method or the gradient descent optimization algorithm implemented in PyTorch. The robustness and adaptability of gradient-based optimization techniques were thoroughly assessed. The learning process minimizes the residual error iteratively, with convergence behaviour and numerical stability analyzed across various parameter configurations. The results demonstrate rapid convergence, proximity to exact solutions, and significant robustness to parameter variability, highlighting the efficacy of gradient descent for solving uncertain linear systems. These findings provide a foundation for the extension of gradient-based methods to more complex systems and broader applications. Furthermore, the existence and uniqueness of the neutrosophic solution to an $n\times n$ linear system were rigorously analyzed, with numerical examples provided to assess the reliability and efficiency of the proposed methods. ]]> Solving Single-Valued Trapezoidal Neutrosophic Linear Equations: An Embedding Approach for Uncertain Systems ahmed abdelaziz elsayed arash kermani kolankeh doi: 10.56578/josa030101 Journal of Operational and Strategic Analytics 03-02-2025 Journal of Operational and Strategic Analytics 03-02-2025 2025 3 1 Article 1 10.56578/josa030101 https://www.acadlore.com/article/JOSA/2025_3_1/josa030101 https://www.acadlore.com/article/JOSA/2024_2_4/josa020405 This study investigates the application of Multi-Criteria Decision-Making (MCDM) techniques in fruit production, specifically focusing on the use of the interval fuzzy rough pivot pairwise relative criteria importance assessment (PIPRECIA) method for criteria evaluation. A total of 11 criteria were evaluated to rank various combinations of plum varieties and rootstocks. The criteria selected represent key aspects of plum production, including phenology, yield, physical fruit characteristics, and the chemical composition and quality of the fruit. Data for the study were collected through surveys of 17 experts and plum producers. The results indicated that the criteria related to overall yield and fruit weight were deemed the most significant, while those concerning the chemical composition and fruit quality were considered of lesser importance. The findings highlight the potential of the interval fuzzy rough PIPRECIA method in addressing both research and managerial challenges in fruit production. It is suggested that future research expand the application of this method to other geographical regions and agricultural sectors. Additionally, the development of accessible software tools featuring user-friendly interfaces could facilitate broader adoption of MCDM techniques in agricultural decision-making. 12-30-2024 <![CDATA[ This study investigates the application of Multi-Criteria Decision-Making (MCDM) techniques in fruit production, specifically focusing on the use of the interval fuzzy rough pivot pairwise relative criteria importance assessment (PIPRECIA) method for criteria evaluation. A total of 11 criteria were evaluated to rank various combinations of plum varieties and rootstocks. The criteria selected represent key aspects of plum production, including phenology, yield, physical fruit characteristics, and the chemical composition and quality of the fruit. Data for the study were collected through surveys of 17 experts and plum producers. The results indicated that the criteria related to overall yield and fruit weight were deemed the most significant, while those concerning the chemical composition and fruit quality were considered of lesser importance. The findings highlight the potential of the interval fuzzy rough PIPRECIA method in addressing both research and managerial challenges in fruit production. It is suggested that future research expand the application of this method to other geographical regions and agricultural sectors. Additionally, the development of accessible software tools featuring user-friendly interfaces could facilitate broader adoption of MCDM techniques in agricultural decision-making. ]]> Evaluation of Criteria in Fruit Production Using the Interval Fuzzy Rough PIPRECIA Method mirjana radović željko stević danijel mijić aleksandra govedarica-lučić radomir bodiroga grujica vico doi: 10.56578/josa020405 Journal of Operational and Strategic Analytics 12-30-2024 Journal of Operational and Strategic Analytics 12-30-2024 2024 2 4 Article 266 10.56578/josa020405 https://www.acadlore.com/article/JOSA/2024_2_4/josa020405 https://www.acadlore.com/article/JOSA/2024_2_4/josa020404 The transportation of dangerous goods (DG) presents significant risks due to their hazardous chemical properties, which, in the event of an accident, can have detrimental effects on the environment, public health, and infrastructure. Although the transport of such materials is generally prohibited, the growing demand for DG transportation over long distances necessitates compliance with stringent international regulations (e.g., ADR, RID). In urban areas, where transport routes may intersect with residential zones, incidents involving DG can lead to severe consequences, including fatalities, environmental damage, evacuation of local populations, and disruptions to traffic. To mitigate these risks, effective risk management is essential, encompassing analysis, assessment, and reduction strategies. Risk assessment for DG transport can be conducted using various quantitative and qualitative methods, with this study employing the Areal Locations of Hazardous Atmospheres (ALOHA) software and Geographic Information System (GIS) tools for both risk evaluation and visualization. The study area is located in the capital of Montenegro, specifically within the Stari Aerodrom District. This research focuses on evaluating the potential impact of DG transport incidents in this area and the consequences of hazardous material releases in confined spaces. Three specific DGs—benzene, chlorine, and methane—are considered, each presenting distinct environmental, health, and property-related risks. Chlorine is selected as the worst-case scenario, with its impact radius extending approximately 10 km from the release point. The primary objective of this study is to provide a comprehensive assessment of the risks associated with DG transportation, highlighting the importance of safety improvements and effective emergency response strategies. The findings underscore the need for enhanced safety measures during transport and the development of more robust emergency management frameworks for DG-related incidents. 12-23-2024 <![CDATA[ The transportation of dangerous goods (DG) presents significant risks due to their hazardous chemical properties, which, in the event of an accident, can have detrimental effects on the environment, public health, and infrastructure. Although the transport of such materials is generally prohibited, the growing demand for DG transportation over long distances necessitates compliance with stringent international regulations (e.g., ADR, RID). In urban areas, where transport routes may intersect with residential zones, incidents involving DG can lead to severe consequences, including fatalities, environmental damage, evacuation of local populations, and disruptions to traffic. To mitigate these risks, effective risk management is essential, encompassing analysis, assessment, and reduction strategies. Risk assessment for DG transport can be conducted using various quantitative and qualitative methods, with this study employing the Areal Locations of Hazardous Atmospheres (ALOHA) software and Geographic Information System (GIS) tools for both risk evaluation and visualization. The study area is located in the capital of Montenegro, specifically within the Stari Aerodrom District. This research focuses on evaluating the potential impact of DG transport incidents in this area and the consequences of hazardous material releases in confined spaces. Three specific DGs—benzene, chlorine, and methane—are considered, each presenting distinct environmental, health, and property-related risks. Chlorine is selected as the worst-case scenario, with its impact radius extending approximately 10 km from the release point. The primary objective of this study is to provide a comprehensive assessment of the risks associated with DG transportation, highlighting the importance of safety improvements and effective emergency response strategies. The findings underscore the need for enhanced safety measures during transport and the development of more robust emergency management frameworks for DG-related incidents. ]]> Risk Assessment in the Transportation of Dangerous Goods: Application of ALOHA and GIS Tools in Montenegro milanko damjanović aleksandra petrović vladimir ilić marko radetić predrag stanojević doi: 10.56578/josa020404 Journal of Operational and Strategic Analytics 12-23-2024 Journal of Operational and Strategic Analytics 12-23-2024 2024 2 4 Article 254 10.56578/josa020404 https://www.acadlore.com/article/JOSA/2024_2_4/josa020404 https://www.acadlore.com/article/JOSA/2024_2_4/josa020403 In today’s volatile and competitive global markets, organizations face numerous challenges to their survival and growth. To navigate these challenges effectively, the adoption of future-oriented, environment-based planning strategies is essential. Such strategies must not only address the identification of key environmental factors but also assess their long-term impacts on the organization, alongside its interaction with these external variables. The survival and sustainable development of an organization depend on a timely understanding of emerging opportunities and market dynamics, the formulation of strategic plans, and the selection of appropriate, effective strategies. This study presents an integrated model designed to evaluate the factors influencing a construction company’s performance, with a focus on conducting a comprehensive risk analysis. The model prioritizes and quantifies the significance of each element within the strengths, weaknesses, opportunities, and threats (SWOT) analysis of the company’s operational context. Furthermore, two fuzzy logic-based Multiple-Attribute Decision-Making (MADM) methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchy Process (AHP), were employed to rank the identified factors. Based on the analysis of the collected data, the final strategic course for the company was derived. The results indicated that the TOPSIS method placed a greater emphasis on the organization's strengths and opportunities, while the AHP approach, despite prioritizing long-term safety considerations, underscored the significance of addressing weaknesses and mitigating threats. This research contributes to the understanding of how fuzzy MADM techniques can be applied to strategic planning in the construction industry, facilitating more informed decision-making processes that align with the evolving demands of the market and ensure organizational resilience. 12-10-2024 <![CDATA[ In today’s volatile and competitive global markets, organizations face numerous challenges to their survival and growth. To navigate these challenges effectively, the adoption of future-oriented, environment-based planning strategies is essential. Such strategies must not only address the identification of key environmental factors but also assess their long-term impacts on the organization, alongside its interaction with these external variables. The survival and sustainable development of an organization depend on a timely understanding of emerging opportunities and market dynamics, the formulation of strategic plans, and the selection of appropriate, effective strategies. This study presents an integrated model designed to evaluate the factors influencing a construction company’s performance, with a focus on conducting a comprehensive risk analysis. The model prioritizes and quantifies the significance of each element within the strengths, weaknesses, opportunities, and threats (SWOT) analysis of the company’s operational context. Furthermore, two fuzzy logic-based Multiple-Attribute Decision-Making (MADM) methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchy Process (AHP), were employed to rank the identified factors. Based on the analysis of the collected data, the final strategic course for the company was derived. The results indicated that the TOPSIS method placed a greater emphasis on the organization's strengths and opportunities, while the AHP approach, despite prioritizing long-term safety considerations, underscored the significance of addressing weaknesses and mitigating threats. This research contributes to the understanding of how fuzzy MADM techniques can be applied to strategic planning in the construction industry, facilitating more informed decision-making processes that align with the evolving demands of the market and ensure organizational resilience. ]]> Comprehensive Strategic Planning for Construction Companies Using Fuzzy MADM Techniques samaneh hoseinpoorian chabok duško tešić doi: 10.56578/josa020403 Journal of Operational and Strategic Analytics 12-10-2024 Journal of Operational and Strategic Analytics 12-10-2024 2024 2 4 Article 235 10.56578/josa020403 https://www.acadlore.com/article/JOSA/2024_2_4/josa020403 https://www.acadlore.com/article/JOSA/2024_2_4/josa020402 The inherent hierarchical and decentralized nature of decision-making within banking systems presents significant challenges in evaluating operational efficiency. This study introduces a novel bi-level programming (BLP) framework, incorporating Stackelberg equilibrium dynamics, to assess the performance of bank branches. By combining with data envelopment analysis (DEA), the proposed BLP-DEA model captures the leader-follower relationship that characterizes banking operations, wherein the leader focuses on marketability and the follower prioritizes profitability. A case study involving 15 Iranian bank branches was employed to demonstrate the model’s capacity to evaluate performance comprehensively at both decision-making levels. The results underscore the model's effectiveness in identifying inefficiencies, analyzing cost structures, and providing actionable insights for performance optimization. This approach offers a robust tool for addressing the complexities associated with decentralized decision-making in hierarchical organizations. The findings have significant implications for both theoretical development and practical application, especially in the context of improving the operational efficiency of banking institutions. 11-22-2024 <![CDATA[ <p>The inherent hierarchical and decentralized nature of decision-making within banking systems presents significant challenges in evaluating operational efficiency. This study introduces a novel bi-level programming (BLP) framework, incorporating Stackelberg equilibrium dynamics, to assess the performance of bank branches. By combining with data envelopment analysis (DEA), the proposed BLP-DEA model captures the leader-follower relationship that characterizes banking operations, wherein the leader focuses on marketability and the follower prioritizes profitability. A case study involving 15 Iranian bank branches was employed to demonstrate the model’s capacity to evaluate performance comprehensively at both decision-making levels. The results underscore the model's effectiveness in identifying inefficiencies, analyzing cost structures, and providing actionable insights for performance optimization. This approach offers a robust tool for addressing the complexities associated with decentralized decision-making in hierarchical organizations. The findings have significant implications for both theoretical development and practical application, especially in the context of improving the operational efficiency of banking institutions.</p> ]]> Hierarchical Efficiency in Banking: Decentralized Bi-Level Programming with Stackelberg Dynamics rita de fátima muniz omar mar cornelio eisa abdolmaleki doi: 10.56578/josa020402 Journal of Operational and Strategic Analytics 11-22-2024 Journal of Operational and Strategic Analytics 11-22-2024 2024 2 4 Article 225 10.56578/josa020402 https://www.acadlore.com/article/JOSA/2024_2_4/josa020402 https://www.acadlore.com/article/JOSA/2024_2_4/josa020401 The identification of optimal stock or portfolio options is a critical concern for investors aiming to maximize profitability within financial markets. With the increasing complexity of available alternatives and the growing volume of financial data, selecting the most suitable investment has become more challenging. Decision-makers often face difficulties in navigating these vast data sets and require robust support tools to simplify and enhance the decision-making process. This study proposes a three-phase approach designed to reduce data complexity and facilitate more detailed analysis. In the initial phase, firms demonstrating low operational efficiency, as indicated by their inventory turnover ratio, were excluded from further consideration. In the subsequent phase, data envelopment analysis (DEA) was employed to assess the efficiency of remaining firms, with those exhibiting efficiency scores lower than one being removed from further investigation. Finally, the third phase involved determining the relative importance of each financial ratio through the calculation of their respective weights, allowing for the ranking of firms based on these adjusted values. The results of this approach provide decision-makers with a refined list of viable investment options, contributing to more informed stock portfolio optimization decisions. 10-19-2024 <![CDATA[ The identification of optimal stock or portfolio options is a critical concern for investors aiming to maximize profitability within financial markets. With the increasing complexity of available alternatives and the growing volume of financial data, selecting the most suitable investment has become more challenging. Decision-makers often face difficulties in navigating these vast data sets and require robust support tools to simplify and enhance the decision-making process. This study proposes a three-phase approach designed to reduce data complexity and facilitate more detailed analysis. In the initial phase, firms demonstrating low operational efficiency, as indicated by their inventory turnover ratio, were excluded from further consideration. In the subsequent phase, data envelopment analysis (DEA) was employed to assess the efficiency of remaining firms, with those exhibiting efficiency scores lower than one being removed from further investigation. Finally, the third phase involved determining the relative importance of each financial ratio through the calculation of their respective weights, allowing for the ranking of firms based on these adjusted values. The results of this approach provide decision-makers with a refined list of viable investment options, contributing to more informed stock portfolio optimization decisions. ]]> A Three-Phase Algorithm for Selecting Optimal Investment Options Based on Financial Ratios of Stock Companies zahra joorbonyan sapan kumar das seyed ali noorkhah ali sorourkhah doi: 10.56578/josa020401 Journal of Operational and Strategic Analytics 10-19-2024 Journal of Operational and Strategic Analytics 10-19-2024 2024 2 4 Article 215 10.56578/josa020401 https://www.acadlore.com/article/JOSA/2024_2_4/josa020401 https://www.acadlore.com/article/JOSA/2024_2_3/josa020305 Fire, as an unpredictable and highly destructive hazard, poses significant risks to densely populated environments such as employee dormitory buildings. This study aims to evaluate fire risks in such facilities and propose effective fire safety management strategies to enhance fire prevention capabilities and evacuation efficiency. An index system of fire risk influencing factors specific to employee dormitory buildings was established through an extensive review of relevant literature and field interviews. The Ordinal Priority Approach (OPA), a multi-attribute decision analysis method based on ordinal data, was employed to quantify the weights of these influencing factors using a linear programming model. Subsequently, fire scenarios were simulated using PyroSim software, focusing on the top two critical influencing factors to assess evacuation times and safety conditions. The analysis identified the condition of fire-fighting facilities, ventilation within dormitory buildings, the use of high-power electrical appliances, and smoking behaviors among employees as key determinants of fire risk. The simulation results indicated that visibility during a fire significantly affects the available safe evacuation time. While natural ventilation was found to moderately mitigate fire spread, its impact was less pronounced compared to the effectiveness of automatic sprinkler systems. The reliability of the simulation outcomes was further validated through expert interviews, ensuring the practical applicability of the findings. Based on the outcomes of risk analysis and scenario simulations, several fire safety improvement measures were proposed. These include upgrading fire-fighting facility standards, optimizing natural ventilation systems, and implementing comprehensive fire safety education and training programs. The insights derived from this research provide a robust scientific foundation and actionable recommendations for the fire risk management of employee dormitory buildings. 09-29-2024 <![CDATA[ Fire, as an unpredictable and highly destructive hazard, poses significant risks to densely populated environments such as employee dormitory buildings. This study aims to evaluate fire risks in such facilities and propose effective fire safety management strategies to enhance fire prevention capabilities and evacuation efficiency. An index system of fire risk influencing factors specific to employee dormitory buildings was established through an extensive review of relevant literature and field interviews. The Ordinal Priority Approach (OPA), a multi-attribute decision analysis method based on ordinal data, was employed to quantify the weights of these influencing factors using a linear programming model. Subsequently, fire scenarios were simulated using PyroSim software, focusing on the top two critical influencing factors to assess evacuation times and safety conditions. The analysis identified the condition of fire-fighting facilities, ventilation within dormitory buildings, the use of high-power electrical appliances, and smoking behaviors among employees as key determinants of fire risk. The simulation results indicated that visibility during a fire significantly affects the available safe evacuation time. While natural ventilation was found to moderately mitigate fire spread, its impact was less pronounced compared to the effectiveness of automatic sprinkler systems. The reliability of the simulation outcomes was further validated through expert interviews, ensuring the practical applicability of the findings. Based on the outcomes of risk analysis and scenario simulations, several fire safety improvement measures were proposed. These include upgrading fire-fighting facility standards, optimizing natural ventilation systems, and implementing comprehensive fire safety education and training programs. The insights derived from this research provide a robust scientific foundation and actionable recommendations for the fire risk management of employee dormitory buildings. ]]> Fire Risk Assessment and Scenario Simulation for Employee Dormitory Buildings qiang li zaohong zhou yunbin sun hongjun he doi: 10.56578/josa020305 Journal of Operational and Strategic Analytics 09-29-2024 Journal of Operational and Strategic Analytics 09-29-2024 2024 2 3 Article 193 10.56578/josa020305 https://www.acadlore.com/article/JOSA/2024_2_3/josa020305 https://www.acadlore.com/article/JOSA/2024_2_3/josa020304 The Logistics Performance Index (LPI) represents a tool developed by the World Bank that is used to measure the efficiency and effectiveness of a country’s logistics sector, and comprises of six components. This indicator is used to compare the logistics performance of different countries, identify challenges in global supply chains, and help policymakers improve their logistics infrastructure and service quality. Given the importance of this indicator, every country aims to achieve a higher LPI score and, consequently, a more favorable ranking. The objective of this paper is to propose a new methodology for calculating the LPI score for transport routes. To validate the proposed methodology, the study analyzes seven cases involving import and export flows from Serbia. Based on the results, the analysis identifies which transport routes achieve the highest scores and which require specific preventive and corrective actions to improve their performance. 09-29-2024 <![CDATA[ The Logistics Performance Index (LPI) represents a tool developed by the World Bank that is used to measure the efficiency and effectiveness of a country’s logistics sector, and comprises of six components. This indicator is used to compare the logistics performance of different countries, identify challenges in global supply chains, and help policymakers improve their logistics infrastructure and service quality. Given the importance of this indicator, every country aims to achieve a higher LPI score and, consequently, a more favorable ranking. The objective of this paper is to propose a new methodology for calculating the LPI score for transport routes. To validate the proposed methodology, the study analyzes seven cases involving import and export flows from Serbia. Based on the results, the analysis identifies which transport routes achieve the highest scores and which require specific preventive and corrective actions to improve their performance. ]]> A Comprehensive Model for Calculating the LPI Index of Key Transport Corridors in Serbia nevena garić vukašin pajić milan andrejić doi: 10.56578/josa020304 Journal of Operational and Strategic Analytics 09-29-2024 Journal of Operational and Strategic Analytics 09-29-2024 2024 2 3 Article 177 10.56578/josa020304 https://www.acadlore.com/article/JOSA/2024_2_3/josa020304 https://www.acadlore.com/article/JOSA/2024_2_3/josa020303 When the word "disaster" is used, it usually refers to both human-caused situations that have a negative impact on the community and its environment as well as natural disasters like hurricanes, earthquakes, floods, and similar phenomena. Good logistics management is crucial to reducing the bad effects of these kinds of circumstances. This typically entails tasks like planning, organizing, acquiring, moving, and other associated duties. The distribution of supplies to impacted individuals in an effort to save lives is the main objective of humanitarian logistics. The location of humanitarian goods and equipment, which are kept in makeshift humanitarian logistics centers, is crucial for ensuring prompt response in such circumstances. As a result, when deciding where to locate these centers, it is crucial to take into account particular local factors. Numerous factors might impact this kind of selection, which is why finding a location for a humanitarian logistics center is considered a multi-criteria challenge. This research suggests using the ADAM (Axial-Distance-Based Aggregated Measurement Method) and SWARA (Stepwise Weight Assessment Ratio Analysis) techniques to solve this kind of issue. An example of their application is provided by a case study that centers on where Serbia's humanitarian logistics hub is located. The creation of a framework and a special set of standards for choosing the locations of humanitarian logistics centers are the main results of this study. This can help decision-makers, authorities, individuals, non-governmental groups, and logistical service providers make well-informed decisions that have the potential to save countless lives. 09-29-2024 <![CDATA[ <p>When the word "disaster" is used, it usually refers to both human-caused situations that have a negative impact on the community and its environment as well as natural disasters like hurricanes, earthquakes, floods, and similar phenomena. Good logistics management is crucial to reducing the bad effects of these kinds of circumstances. This typically entails tasks like planning, organizing, acquiring, moving, and other associated duties. The distribution of supplies to impacted individuals in an effort to save lives is the main objective of humanitarian logistics. The location of humanitarian goods and equipment, which are kept in makeshift humanitarian logistics centers, is crucial for ensuring prompt response in such circumstances. As a result, when deciding where to locate these centers, it is crucial to take into account particular local factors. Numerous factors might impact this kind of selection, which is why finding a location for a humanitarian logistics center is considered a multi-criteria challenge. This research suggests using the ADAM (Axial-Distance-Based Aggregated Measurement Method) and SWARA (Stepwise Weight Assessment Ratio Analysis) techniques to solve this kind of issue. An example of their application is provided by a case study that centers on where Serbia's humanitarian logistics hub is located. The creation of a framework and a special set of standards for choosing the locations of humanitarian logistics centers are the main results of this study. This can help decision-makers, authorities, individuals, non-governmental groups, and logistical service providers make well-informed decisions that have the potential to save countless lives.</p> ]]> Application of a Hybrid MCDM Model for Locating a Humanitarian Logistics Center mladen krstić snežana tadić anica spajić doi: 10.56578/josa020303 Journal of Operational and Strategic Analytics 09-29-2024 Journal of Operational and Strategic Analytics 09-29-2024 2024 2 3 Article 160 10.56578/josa020303 https://www.acadlore.com/article/JOSA/2024_2_3/josa020303 https://www.acadlore.com/article/JOSA/2024_2_3/josa020302 This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying $M / D / 1$ queueing system. Currently, the only known solution to this problem is through simulation. However, a study proposes a constant ratio, $\beta$ (Ismail's ratio), that relates the time-dependent mean arrival and mean service rates, offering an exact analytical solution. The stability dynamics of the time-varying $M / D / 1$ queueing system are then examined numerically in relation to time, $\beta$, and the queueing parameters. On another note, many potential queueing-theoretic applications to traffic management optimization are provided. The paper concludes with a summary, combined with open problems and future research pathways. 09-29-2024 <![CDATA[ This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying $M / D / 1$ queueing system. Currently, the only known solution to this problem is through simulation. However, a study proposes a constant ratio, $\beta$ (Ismail's ratio), that relates the time-dependent mean arrival and mean service rates, offering an exact analytical solution. The stability dynamics of the time-varying $M / D / 1$ queueing system are then examined numerically in relation to time, $\beta$, and the queueing parameters. On another note, many potential queueing-theoretic applications to traffic management optimization are provided. The paper concludes with a summary, combined with open problems and future research pathways. ]]> Ismail’s Ratio Conquers New Horizons: The Non-Stationary $M/D/1$ Queue’s State Variable Closed Form Expression with Queueing Applications to Traffic Management Optimization ismail a mageed doi: 10.56578/josa020302 Journal of Operational and Strategic Analytics 09-29-2024 Journal of Operational and Strategic Analytics 09-29-2024 2024 2 3 Article 144 10.56578/josa020302 https://www.acadlore.com/article/JOSA/2024_2_3/josa020302 https://www.acadlore.com/article/JOSA/2024_2_3/josa020301 As global focus on climate change intensifies, carbon credits have become an important tool for reducing greenhouse gas emissions. Africa, with its abundant natural resources and potential for sustainable development, is well-positioned to capitalize on this growing market. This article explores how Africa can enhance its participation in the carbon credit market, transforming environmental initiatives into economic opportunities by addressing key implementation challenges. By utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method within an interval-valued spherical fuzzy (IVSF) framework, the study supports collective decision-making. It identifies three crucial factors: access to financing issue, the absence of clear policies and legal frameworks, and the lack of capacity and expertise within governments, businesses, and communities. The research provides practical recommendations for governments aiming to effectively implement the carbon credit concept. 09-24-2024 <![CDATA[ As global focus on climate change intensifies, carbon credits have become an important tool for reducing greenhouse gas emissions. Africa, with its abundant natural resources and potential for sustainable development, is well-positioned to capitalize on this growing market. This article explores how Africa can enhance its participation in the carbon credit market, transforming environmental initiatives into economic opportunities by addressing key implementation challenges. By utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method within an interval-valued spherical fuzzy (IVSF) framework, the study supports collective decision-making. It identifies three crucial factors: access to financing issue, the absence of clear policies and legal frameworks, and the lack of capacity and expertise within governments, businesses, and communities. The research provides practical recommendations for governments aiming to effectively implement the carbon credit concept. ]]> Addressing the Crucial Factors Affecting the Implementation of Carbon Credit Concept Using a Comprehensive Decision-Making Analysis: A Case Study qian su yanjun qiu mouhamed bayane bouraima babatounde ifred paterne zonon ibrahim badi ndiema kevin maraka doi: 10.56578/josa020301 Journal of Operational and Strategic Analytics 09-24-2024 Journal of Operational and Strategic Analytics 09-24-2024 2024 2 3 Article 136 10.56578/josa020301 https://www.acadlore.com/article/JOSA/2024_2_3/josa020301 https://www.acadlore.com/article/JOSA/2024_2_2/josa020205 The COVID-19 pandemic has prompted extensive modeling efforts worldwide, aimed at understanding its progression and the myriad factors influencing its spread across diverse communities. The necessity for tailored control measures, varying significantly by region, became apparent early in the pandemic, leading to the implementation of diverse strategies to manage the virus both in the short and long term. The World Health Organization (WHO) has faced considerable challenges in mitigating the impact of COVID-19, necessitating adaptable and localized public health responses. Traditional mathematical models, often employing classical integer-order derivatives with real numbers, have been instrumental in analyzing the virus's spread; however, these models inadequately address the fading memory effects inherent in such complex scenarios. To overcome these limitations, fuzzy sets (FSs) were introduced, offering a robust framework for managing the uncertainty that characterizes the pandemic’s dynamics. This research introduces innovative methods based on complex Fermatean FSs (CFFSs), alongside their corresponding geometric aggregation operators, including the complex Fermatean fuzzy weighted geometric aggregation (CFFWGA) operator, the complex Fermatean fuzzy ordered weighted geometric aggregation (CFFOWGA) operator, and the complex Fermatean fuzzy hybrid geometric aggregation (CFFHGA) operator. These advanced techniques are proposed as effective tools in the strategic decision-making process for reducing the spread of COVID-19. A compelling case study on COVID-19 vaccine selection was presented, demonstrating the practical applicability and superiority of these methods, effectively bridging theoretical models with real-world applications. 06-29-2024 <![CDATA[ The COVID-19 pandemic has prompted extensive modeling efforts worldwide, aimed at understanding its progression and the myriad factors influencing its spread across diverse communities. The necessity for tailored control measures, varying significantly by region, became apparent early in the pandemic, leading to the implementation of diverse strategies to manage the virus both in the short and long term. The World Health Organization (WHO) has faced considerable challenges in mitigating the impact of COVID-19, necessitating adaptable and localized public health responses. Traditional mathematical models, often employing classical integer-order derivatives with real numbers, have been instrumental in analyzing the virus's spread; however, these models inadequately address the fading memory effects inherent in such complex scenarios. To overcome these limitations, fuzzy sets (FSs) were introduced, offering a robust framework for managing the uncertainty that characterizes the pandemic’s dynamics. This research introduces innovative methods based on complex Fermatean FSs (CFFSs), alongside their corresponding geometric aggregation operators, including the complex Fermatean fuzzy weighted geometric aggregation (CFFWGA) operator, the complex Fermatean fuzzy ordered weighted geometric aggregation (CFFOWGA) operator, and the complex Fermatean fuzzy hybrid geometric aggregation (CFFHGA) operator. These advanced techniques are proposed as effective tools in the strategic decision-making process for reducing the spread of COVID-19. A compelling case study on COVID-19 vaccine selection was presented, demonstrating the practical applicability and superiority of these methods, effectively bridging theoretical models with real-world applications. ]]> Complex Fermatean Fuzzy Models and Their Algebraic Aggregation Operators in Decision-Making: A Case Study on COVID-19 Vaccine Selection rifaqat ali khaista rahman jan muhammad doi: 10.56578/josa020205 Journal of Operational and Strategic Analytics 06-29-2024 Journal of Operational and Strategic Analytics 06-29-2024 2024 2 2 Article 119 10.56578/josa020205 https://www.acadlore.com/article/JOSA/2024_2_2/josa020205 https://www.acadlore.com/article/JOSA/2024_2_2/josa020204 Confidence sets provide a robust method for addressing the uncertainty inherent in the membership degrees of elements within fuzzy sets (FSs). These sets enhance the capability of FSs to manage imprecise or uncertain data systematically. Analogous to repeated experimentation, the interpretation of confidence sets remains valid before sample observation. However, once the sample is examined, all confidence sets exclusively encompass parameter values of either 1 or 0. This study introduces novel techniques in the domain of confidence levels, specifically the Confidence Complex Polytopic Fuzzy Weighted Averaging (CCPoFWA) operator, confidence complex polytopic fuzzy ordered weighted averaging (CCPoFOWA) operator, and Confidence Complex Polytopic Fuzzy Hybrid Averaging (CCPoFHA) operator. These aggregation operators are indispensable tools in data analysis and decision-making, aiding in the understanding of complex systems across diverse fields. They facilitate the extraction of valuable insights from extensive datasets and streamline the presentation of information to enhance decision support. The efficacy and utility of the proposed methods are demonstrated through a detailed illustrative example, underscoring their potential in strategic decision-making for the placement of nuclear power plants in Pakistan. 06-29-2024 <![CDATA[ Confidence sets provide a robust method for addressing the uncertainty inherent in the membership degrees of elements within fuzzy sets (FSs). These sets enhance the capability of FSs to manage imprecise or uncertain data systematically. Analogous to repeated experimentation, the interpretation of confidence sets remains valid before sample observation. However, once the sample is examined, all confidence sets exclusively encompass parameter values of either 1 or 0. This study introduces novel techniques in the domain of confidence levels, specifically the Confidence Complex Polytopic Fuzzy Weighted Averaging (CCPoFWA) operator, confidence complex polytopic fuzzy ordered weighted averaging (CCPoFOWA) operator, and Confidence Complex Polytopic Fuzzy Hybrid Averaging (CCPoFHA) operator. These aggregation operators are indispensable tools in data analysis and decision-making, aiding in the understanding of complex systems across diverse fields. They facilitate the extraction of valuable insights from extensive datasets and streamline the presentation of information to enhance decision support. The efficacy and utility of the proposed methods are demonstrated through a detailed illustrative example, underscoring their potential in strategic decision-making for the placement of nuclear power plants in Pakistan. ]]> Strategic Placement of Nuclear Power Plants in Pakistan: A Complex Polytopic Fuzzy Model Approach with Confidence Level Assessment khaista rahman doi: 10.56578/josa020204 Journal of Operational and Strategic Analytics 06-29-2024 Journal of Operational and Strategic Analytics 06-29-2024 2024 2 2 Article 107 10.56578/josa020204 https://www.acadlore.com/article/JOSA/2024_2_2/josa020204 https://www.acadlore.com/article/JOSA/2024_2_2/josa020203 The pressing need to reduce reliance on petroleum in the energy sector and the increasing demand for environmental protection are driving research and practical endeavors in the management of renewable supply chains. Professionals, global institutions and scholars have widely acknowledged the importance of studying the correlation, between the performance of supply chains and renewable energy sources. It's important to delve into the articles in terms of the methodologies that have been used, the principal concerns addressed, the specific renewable energy sources focused on, and the performance indicators employed to optimize supply chains for renewable energies. This paper provides an analysis that improves the understanding of research in the realm of quantitative decision making for renewable energy supply chains. The analysis commences by searching for articles published. Subsequently, they are narrowed down to those that are most relevant. The article also addresses knowledge gaps in the literature. The findings provide a reference for researchers who are considering conducting studies in this area. 05-14-2024 <![CDATA[ The pressing need to reduce reliance on petroleum in the energy sector and the increasing demand for environmental protection are driving research and practical endeavors in the management of renewable supply chains. Professionals, global institutions and scholars have widely acknowledged the importance of studying the correlation, between the performance of supply chains and renewable energy sources. It's important to delve into the articles in terms of the methodologies that have been used, the principal concerns addressed, the specific renewable energy sources focused on, and the performance indicators employed to optimize supply chains for renewable energies. This paper provides an analysis that improves the understanding of research in the realm of quantitative decision making for renewable energy supply chains. The analysis commences by searching for articles published. Subsequently, they are narrowed down to those that are most relevant. The article also addresses knowledge gaps in the literature. The findings provide a reference for researchers who are considering conducting studies in this area. ]]> Multi-Criteria Analysis Techniques to Assist Decision-Making in Renewable Energy Supply Chains: A Review maedeh fasihi doi: 10.56578/josa020203 Journal of Operational and Strategic Analytics 05-14-2024 Journal of Operational and Strategic Analytics 05-14-2024 2024 2 2 Article 92 10.56578/josa020203 https://www.acadlore.com/article/JOSA/2024_2_2/josa020203 https://www.acadlore.com/article/JOSA/2024_2_2/josa020202 This study was undertaken to elucidate the influence of self-management on the productivity levels of personnel within the Water and Wastewater Department, District 2, Tehran, utilizing a descriptive survey method that engaged 119 respondents. The assessment was founded on the administration of meticulously validated questionnaires, with subsequent statistical analysis conducted using Statistical Package for the Social Sciences (SPSS). The analysis included the Kolmogorov-Smirnov test to confirm the normal distribution of the variables, namely, self-management strategies and productivity levels, and the Pearson-Spearman tests to evaluate correlations. The findings, underscored by Cronbach's Alpha values of 0.879 for self-management strategies and 0.906 for productivity levels, confirmed the hypothesis of a significant positive impact of self-management on workforce productivity. Notably, the natural reward strategy was identified as having the least effect on ameliorating workplace conditions. This investigation contributes to the body of knowledge by highlighting the critical role of self-management practices in enhancing the efficiency of public sector operations. The insights garnered from this study pave the way for the implementation of strategic self-management practices aimed at boosting productivity within public sector entities. 05-07-2024 <![CDATA[ This study was undertaken to elucidate the influence of self-management on the productivity levels of personnel within the Water and Wastewater Department, District 2, Tehran, utilizing a descriptive survey method that engaged 119 respondents. The assessment was founded on the administration of meticulously validated questionnaires, with subsequent statistical analysis conducted using Statistical Package for the Social Sciences (SPSS). The analysis included the Kolmogorov-Smirnov test to confirm the normal distribution of the variables, namely, self-management strategies and productivity levels, and the Pearson-Spearman tests to evaluate correlations. The findings, underscored by Cronbach's Alpha values of 0.879 for self-management strategies and 0.906 for productivity levels, confirmed the hypothesis of a significant positive impact of self-management on workforce productivity. Notably, the natural reward strategy was identified as having the least effect on ameliorating workplace conditions. This investigation contributes to the body of knowledge by highlighting the critical role of self-management practices in enhancing the efficiency of public sector operations. The insights garnered from this study pave the way for the implementation of strategic self-management practices aimed at boosting productivity within public sector entities. ]]> Leveraging Self-Management for Enhanced Productivity: Insights from Tehran's Water Sector sahand abdinematabad roghaye ebadikhah reza raeinojehdehi doi: 10.56578/josa020202 Journal of Operational and Strategic Analytics 05-07-2024 Journal of Operational and Strategic Analytics 05-07-2024 2024 2 2 Article 84 10.56578/josa020202 https://www.acadlore.com/article/JOSA/2024_2_2/josa020202 https://www.acadlore.com/article/JOSA/2024_2_2/josa020201 In the realm of understanding consumer purchasing behaviors and refining decision-making across diverse sectors, Market Basket Analysis (MBA) emerges as a pivotal technique. Traditional algorithms, such as Apriori and Frequent Pattern Growth (FP-Growth), face challenges with computational efficiency, particularly under low minimal support settings, which precipitates an excess of weak association rules. This study introduces an innovative approach, termed Customer-Centric (CC)-MBA, which enhances the identification of robust association rules through the integration of consumer segmentation. By employing Recency, Frequency, and Monetary (RFM) analysis coupled with K-means clustering, customers are categorized based on their purchasing patterns, focusing on segments of substantial value. This targeted approach yields association rules that are not only more relevant but also more actionable compared to those derived from conventional MBA methodologies. The superiority of CC-MBA is demonstrated through its ability to discern more significant association rules, as evidenced by enhanced metrics of support and confidence. Additionally, the effectiveness of CC-MBA is further evaluated using lift and conviction metrics, which respectively measure the observed co-occurrence ratio to that expected by chance and the strength of association rules beyond random occurrences. The application of CC-MBA not only streamlines the analytical process by reducing computational demands but also provides more nuanced insights by prioritizing high-value customer segments. The practical implications of these findings are manifold; businesses can leverage this refined understanding to improve product positioning, devise targeted promotions, and tailor marketing strategies, thereby augmenting consumer satisfaction and facilitating revenue growth. 04-29-2024 <![CDATA[ In the realm of understanding consumer purchasing behaviors and refining decision-making across diverse sectors, Market Basket Analysis (MBA) emerges as a pivotal technique. Traditional algorithms, such as Apriori and Frequent Pattern Growth (FP-Growth), face challenges with computational efficiency, particularly under low minimal support settings, which precipitates an excess of weak association rules. This study introduces an innovative approach, termed Customer-Centric (CC)-MBA, which enhances the identification of robust association rules through the integration of consumer segmentation. By employing Recency, Frequency, and Monetary (RFM) analysis coupled with K-means clustering, customers are categorized based on their purchasing patterns, focusing on segments of substantial value. This targeted approach yields association rules that are not only more relevant but also more actionable compared to those derived from conventional MBA methodologies. The superiority of CC-MBA is demonstrated through its ability to discern more significant association rules, as evidenced by enhanced metrics of support and confidence. Additionally, the effectiveness of CC-MBA is further evaluated using lift and conviction metrics, which respectively measure the observed co-occurrence ratio to that expected by chance and the strength of association rules beyond random occurrences. The application of CC-MBA not only streamlines the analytical process by reducing computational demands but also provides more nuanced insights by prioritizing high-value customer segments. The practical implications of these findings are manifold; businesses can leverage this refined understanding to improve product positioning, devise targeted promotions, and tailor marketing strategies, thereby augmenting consumer satisfaction and facilitating revenue growth. ]]> Optimizing Decision-Making Through Customer-Centric Market Basket Analysis md jiabul hoque md. saiful islam syed abrar mohtasim doi: 10.56578/josa020201 Journal of Operational and Strategic Analytics 04-29-2024 Journal of Operational and Strategic Analytics 04-29-2024 2024 2 2 Article 72 10.56578/josa020201 https://www.acadlore.com/article/JOSA/2024_2_2/josa020201 https://www.acadlore.com/article/JOSA/2024_2_1/josa020106 An innovative framework is introduced for the enhancement of efficiency within emergency departments (EDs), utilizing an integration of simulation and fuzzy Multi-Criteria Decision-Making (MCDM). A discrete event simulation (DES) model was developed, capturing the intricate dynamics characteristic of ED operations with high fidelity. This model's integration with the Analytic Hierarchy Process (AHP) and the Elimination and Choice Expressing Reality (ELECTRE) method, within a fuzzy context, facilitated a critical evaluation and optimization of the decision-making processes inherent in EDs. The incorporation of these methodologies yielded significant improvements in patient flow and service quality, highlighting the substantial potential of marrying simulation with fuzzy MCDM to achieve operational excellence in healthcare settings. The study stands as a contribution to the enhancement of ED operations, offering a versatile methodology with potential for adaptation across diverse healthcare environments. This approach underscores the imperative of employing a nuanced, integrated strategy to navigate the complexities of healthcare service delivery, ensuring an equilibrium between operational efficiency and the quality of patient care. 03-28-2024 <![CDATA[ An innovative framework is introduced for the enhancement of efficiency within emergency departments (EDs), utilizing an integration of simulation and fuzzy Multi-Criteria Decision-Making (MCDM). A discrete event simulation (DES) model was developed, capturing the intricate dynamics characteristic of ED operations with high fidelity. This model's integration with the Analytic Hierarchy Process (AHP) and the Elimination and Choice Expressing Reality (ELECTRE) method, within a fuzzy context, facilitated a critical evaluation and optimization of the decision-making processes inherent in EDs. The incorporation of these methodologies yielded significant improvements in patient flow and service quality, highlighting the substantial potential of marrying simulation with fuzzy MCDM to achieve operational excellence in healthcare settings. The study stands as a contribution to the enhancement of ED operations, offering a versatile methodology with potential for adaptation across diverse healthcare environments. This approach underscores the imperative of employing a nuanced, integrated strategy to navigate the complexities of healthcare service delivery, ensuring an equilibrium between operational efficiency and the quality of patient care. ]]> Enhancing Emergency Department Efficiency Through Simulation and Fuzzy Multi-Criteria Decision-Making Integration houman lazarashouri seyyed esmaeil najafi doi: 10.56578/josa020106 Journal of Operational and Strategic Analytics 03-28-2024 Journal of Operational and Strategic Analytics 03-28-2024 2024 2 1 Article 56 10.56578/josa020106 https://www.acadlore.com/article/JOSA/2024_2_1/josa020106 https://www.acadlore.com/article/JOSA/2024_2_1/josa020105 In the realm of cybersecurity, the formulation of comprehensive strategies is imperative for multinational corporations to protect against pervasive cyber threats. Recent developments in the field of intuitionistic multi-fuzzy sets (IMFSs) have heralded q-rung orthopair multi-fuzzy sets (MFSs) as a pivotal tool for encapsulating ambiguity and uncertainty within complex scenarios. The essence of this study lies in the introduction of two innovative distance measures tailored for q-rung orthopair MFSs (q-ROM$^{k}$FSs) of dimension k, enhancing the capacity to delineate distinctions between such sets effectively. Employing score functions pertinent to q-ROM$^{k}$FSs, this research extends its application to the sphere of Multi-Attribute Decision Making (MADM), presenting a methodological advancement in decision-making processes. The efficacy of the proposed measures is elucidated through a comparative analysis with existing methodologies in MADM, thereby underscoring the superiority of the introduced approach. This investigation not only contributes to the enrichment of the theoretical underpinnings of q-ROMFSs but also propels their practical application in cybersecurity strategy formulation for multinational entities. The study employs the Euclidean and Hamming distance measures as benchmarks, supplemented by the development of a score and accuracy function, to furnish a comprehensive tool for addressing cybersecurity challenges. 03-27-2024 <![CDATA[ <p>In the realm of cybersecurity, the formulation of comprehensive strategies is imperative for multinational corporations to protect against pervasive cyber threats. Recent developments in the field of intuitionistic multi-fuzzy sets (IMFSs) have heralded q-rung orthopair multi-fuzzy sets (MFSs) as a pivotal tool for encapsulating ambiguity and uncertainty within complex scenarios. The essence of this study lies in the introduction of two innovative distance measures tailored for q-rung orthopair MFSs (q-ROM$^{k}$FSs) of dimension k, enhancing the capacity to delineate distinctions between such sets effectively. Employing score functions pertinent to q-ROM$^{k}$FSs, this research extends its application to the sphere of Multi-Attribute Decision Making (MADM), presenting a methodological advancement in decision-making processes. The efficacy of the proposed measures is elucidated through a comparative analysis with existing methodologies in MADM, thereby underscoring the superiority of the introduced approach. This investigation not only contributes to the enrichment of the theoretical underpinnings of q-ROMFSs but also propels their practical application in cybersecurity strategy formulation for multinational entities. The study employs the Euclidean and Hamming distance measures as benchmarks, supplemented by the development of a score and accuracy function, to furnish a comprehensive tool for addressing cybersecurity challenges.</p> ]]> Advancing Cybersecurity Strategies for Multinational Corporations: Novel Distance Measures in q-Rung Orthopair Multi-Fuzzy Systems pethaperumal mahalakshmi jayakumar vimala kannan jeevitha shanmugam nithya sri doi: 10.56578/josa020105 Journal of Operational and Strategic Analytics 03-27-2024 Journal of Operational and Strategic Analytics 03-27-2024 2024 2 1 Article 49 10.56578/josa020105 https://www.acadlore.com/article/JOSA/2024_2_1/josa020105 https://www.acadlore.com/article/JOSA/2024_2_1/josa020104 In the realm of general insurance in India, an econometric investigation was conducted to estimate the revenue efficiency across a selection of 15 prominent, diversified general insurance entities for the fiscal years 2011-12 to 2016-17. Utilizing a semi-parametric methodology, the revenue frontier was constructed under the GAM framework, while the variance components were estimated employing the method of moments. This analysis further explored the influence of revenue efficiency on critical profitability metrics, namely return on equity (ROE) and return on assets (ROA), through the application of instrumental variable regression. The findings provide pivotal insights into the dynamics of revenue efficiency and its consequential impact on the financial performance of general insurance companies in India, offering a substantial contribution to the literature on insurance economics and the methodology of efficiency measurement. The research underscores the significance of adopting semi-parametric models for a nuanced understanding of revenue efficiency, thus paving the way for enhanced strategic decision-making in the insurance sector. 03-26-2024 <![CDATA[ In the realm of general insurance in India, an econometric investigation was conducted to estimate the revenue efficiency across a selection of 15 prominent, diversified general insurance entities for the fiscal years 2011-12 to 2016-17. Utilizing a semi-parametric methodology, the revenue frontier was constructed under the GAM framework, while the variance components were estimated employing the method of moments. This analysis further explored the influence of revenue efficiency on critical profitability metrics, namely return on equity (ROE) and return on assets (ROA), through the application of instrumental variable regression. The findings provide pivotal insights into the dynamics of revenue efficiency and its consequential impact on the financial performance of general insurance companies in India, offering a substantial contribution to the literature on insurance economics and the methodology of efficiency measurement. The research underscores the significance of adopting semi-parametric models for a nuanced understanding of revenue efficiency, thus paving the way for enhanced strategic decision-making in the insurance sector. ]]> Estimating Revenue Efficiency in Indian General Insurance Companies: A Semi-parametric Econometric Approach ram pratap sinha doi: 10.56578/josa020104 Journal of Operational and Strategic Analytics 03-26-2024 Journal of Operational and Strategic Analytics 03-26-2024 2024 2 1 Article 36 10.56578/josa020104 https://www.acadlore.com/article/JOSA/2024_2_1/josa020104 https://www.acadlore.com/article/JOSA/2024_2_1/josa020103 The Spherical fuzzy rough set (SFRS), which is based on approximations and is handled in this work, is a key idea for handling uncertainty when data is taken from real-world situations. The most adaptable operational laws based on the parameter for fuzzy frameworks are the Aczel-Alsina t-norm (AATN) and Aczel-Alsina t-conorm (AATCN), which are crucial for data interpolation. In this paper, operators based on AATN and AATCN are developed: spherical fuzzy rough Aczel-Alsina weighted geometric (SFRAAWG), spherical fuzzy rough Aczel-Alsina ordered weighted geometric (SFRAAOWG), and spherical fuzzy rough Aczel-Alsina hybrid weighted geometric (SFRAAHWG). A few fundamental properties of the generated SFRAAWG, SFRAAOWG, and SFRAAHWG operators are defined and given examples. The multi-criteria decision-making (MADM) problem is applied to the developed SFRAAWG operator. Additionally, the sensitivity of the SFRAAG operator is examined. The developed AOs are compared to a few pre-existing AOs and their significance is evaluated. 03-19-2024 <![CDATA[ The Spherical fuzzy rough set (SFRS), which is based on approximations and is handled in this work, is a key idea for handling uncertainty when data is taken from real-world situations. The most adaptable operational laws based on the parameter for fuzzy frameworks are the Aczel-Alsina t-norm (AATN) and Aczel-Alsina t-conorm (AATCN), which are crucial for data interpolation. In this paper, operators based on AATN and AATCN are developed: spherical fuzzy rough Aczel-Alsina weighted geometric (SFRAAWG), spherical fuzzy rough Aczel-Alsina ordered weighted geometric (SFRAAOWG), and spherical fuzzy rough Aczel-Alsina hybrid weighted geometric (SFRAAHWG). A few fundamental properties of the generated SFRAAWG, SFRAAOWG, and SFRAAHWG operators are defined and given examples. The multi-criteria decision-making (MADM) problem is applied to the developed SFRAAWG operator. Additionally, the sensitivity of the SFRAAG operator is examined. The developed AOs are compared to a few pre-existing AOs and their significance is evaluated. ]]> Aczel-Alsina Aggregation Operators on Spherical Fuzzy Rough Set and Their Application Section of Solar Panel mehwish sarfraz doi: 10.56578/josa020103 Journal of Operational and Strategic Analytics 03-19-2024 Journal of Operational and Strategic Analytics 03-19-2024 2024 2 1 Article 21 10.56578/josa020103 https://www.acadlore.com/article/JOSA/2024_2_1/josa020103 https://www.acadlore.com/article/JOSA/2024_2_1/josa020102 This investigation explores the scheduling of $n$ jobs on a single machine, where each job possesses a common due date, and processing time is characterized by pentagonal fuzzy numbers (PFNs). The primary objective is to minimize the aggregate of inventory holding and penalty costs, addressing the critical impact of earliness and tardiness on profitability. It is identified that earliness leads to increased inventory carrying costs and potential degradation in product quality, whereas tardiness undermines customer goodwill and inflicts reputational damage through delayed payments. Consequently, the scheduling dilemma that seeks to minimize the combined penalties of earliness and tardiness, whilst adhering to a common due date on a single machine, emerges as a pivotal and challenging endeavor in optimizing goods delivery within production settings. Recognized as a non-deterministic polynomial-time hardness (NP-hard) problem, this task underscores the complexity and competitive nature inherent in manufacturing operations. To navigate the uncertainties embedded in this problem, a fuzzy logic approach, augmented by a heuristic algorithm, is employed. Through this methodology, the problem is addressed in a manner that encapsulates the vagueness and imprecision inherent in processing time, thereby facilitating more resilient and adaptable scheduling decisions. The efficacy of this approach is demonstrated via a computational example, underscoring its potential to enhance decision-making in the realm of job scheduling. 03-12-2024 <![CDATA[ This investigation explores the scheduling of $n$ jobs on a single machine, where each job possesses a common due date, and processing time is characterized by pentagonal fuzzy numbers (PFNs). The primary objective is to minimize the aggregate of inventory holding and penalty costs, addressing the critical impact of earliness and tardiness on profitability. It is identified that earliness leads to increased inventory carrying costs and potential degradation in product quality, whereas tardiness undermines customer goodwill and inflicts reputational damage through delayed payments. Consequently, the scheduling dilemma that seeks to minimize the combined penalties of earliness and tardiness, whilst adhering to a common due date on a single machine, emerges as a pivotal and challenging endeavor in optimizing goods delivery within production settings. Recognized as a non-deterministic polynomial-time hardness (NP-hard) problem, this task underscores the complexity and competitive nature inherent in manufacturing operations. To navigate the uncertainties embedded in this problem, a fuzzy logic approach, augmented by a heuristic algorithm, is employed. Through this methodology, the problem is addressed in a manner that encapsulates the vagueness and imprecision inherent in processing time, thereby facilitating more resilient and adaptable scheduling decisions. The efficacy of this approach is demonstrated via a computational example, underscoring its potential to enhance decision-making in the realm of job scheduling. ]]> Towards a Fuzzy Approach for Optimizing Single Machine Common Due Date Scheduling Problem under Uncertainty hamiden abd el-wahed khalifa robert s. keyser doi: 10.56578/josa020102 Journal of Operational and Strategic Analytics 03-12-2024 Journal of Operational and Strategic Analytics 03-12-2024 2024 2 1 Article 11 10.56578/josa020102 https://www.acadlore.com/article/JOSA/2024_2_1/josa020102 https://www.acadlore.com/article/JOSA/2024_2_1/josa020101 This study delves into the intricate relationships among returns of diverse indices within the Tehran stock market, employing both Pearson and partial correlation coefficients as analytical tools. Utilizing monthly data from fourteen capital market indices, the investigation applies the k-means method for clustering based on four critical attributes: risk, efficiency, average industry index, and the number of companies within each industry. The findings reveal that when the total index is considered as a controlling variable, the partial correlation analysis yields distinct insights into the interconnections among market indices, thereby highlighting the significant influence of the total index on these relationships. Moreover, the clustering analysis categorizes the indices into three distinct groups: the first cluster exclusively comprises the total index; the second cluster includes indices from the automobile, pharmaceutical, metal, cement, chemical, and food sectors; whereas the remaining indices are allocated to the third cluster. This multifaceted approach not only elucidates the dynamic interplay between different stock market indices but also underscores the variability in their interrelations when viewed through the lens of a controlled variable. The study's methodological rigor and its innovative use of multiscale partial correlation analysis contribute to a deeper understanding of the factors shaping the Tehran stock market's behavior, offering valuable insights for investors, policymakers, and scholars alike. 03-11-2024 <![CDATA[ This study delves into the intricate relationships among returns of diverse indices within the Tehran stock market, employing both Pearson and partial correlation coefficients as analytical tools. Utilizing monthly data from fourteen capital market indices, the investigation applies the k-means method for clustering based on four critical attributes: risk, efficiency, average industry index, and the number of companies within each industry. The findings reveal that when the total index is considered as a controlling variable, the partial correlation analysis yields distinct insights into the interconnections among market indices, thereby highlighting the significant influence of the total index on these relationships. Moreover, the clustering analysis categorizes the indices into three distinct groups: the first cluster exclusively comprises the total index; the second cluster includes indices from the automobile, pharmaceutical, metal, cement, chemical, and food sectors; whereas the remaining indices are allocated to the third cluster. This multifaceted approach not only elucidates the dynamic interplay between different stock market indices but also underscores the variability in their interrelations when viewed through the lens of a controlled variable. The study's methodological rigor and its innovative use of multiscale partial correlation analysis contribute to a deeper understanding of the factors shaping the Tehran stock market's behavior, offering valuable insights for investors, policymakers, and scholars alike. ]]> Multiscale Partial Correlation Analysis of Tehran Stock Market Indices: Clustering and Inter-Index Relationships mohsen imeni zongke bao victoria nozick doi: 10.56578/josa020101 Journal of Operational and Strategic Analytics 03-11-2024 Journal of Operational and Strategic Analytics 03-11-2024 2024 2 1 Article 1 10.56578/josa020101 https://www.acadlore.com/article/JOSA/2024_2_1/josa020101 https://www.acadlore.com/article/JOSA/2023_1_4/josa010406 The onset of the global pandemic has underscored the pivotal role of logistics, bolstered by information and communication technologies, in the resilience of supply chain networks. This study investigates the transformative impact of the COVID-19 pandemic on these networks, with a focus on the resultant operational challenges and labor shortages experienced in Poland – a critical hub in European supply chains. The research delves into how cooperative game theory can be strategically applied to address workforce deficits, particularly in sectors vital to Poland's economy, such as food and healthcare. In the context of reduced operations triggered by illness, fatalities, and preventive measures, including travel restrictions, this study elucidates the operational dynamics within supply chain networks through game theory frameworks. It scrutinizes the strategies implemented by major corporations, including Amazon, DHL, Post Office, KFC, and McDonald's, to navigate these challenges. The methodology encompasses an analysis of the network structure of supply chain game theory, tailored to the operational confines of Poland's logistics sector, acknowledging its role as Europe's breadbasket. The findings reveal various approaches to counteract labor shortages exacerbated by the pandemic, drawing parallels with similar challenges in regions like Africa, Asia, Ukraine, Turkey, and India. The study highlights the diverse impacts of workforce disruptions on commodity prices and the revenues of logistics companies within the supply network economy. These insights contribute to a broader understanding of the financial and operational implications of cooperative game theory in the context of global health emergencies. Conclusively, this research augments existing literature by demonstrating the applicability of cooperative game theory in addressing labor shortages under pandemic-induced constraints. It presents a comprehensive analysis of the strategies employed by key players in the logistics sector, offering valuable perspectives on mitigating operational disruptions in times of crisis. 12-30-2023 <![CDATA[ The onset of the global pandemic has underscored the pivotal role of logistics, bolstered by information and communication technologies, in the resilience of supply chain networks. This study investigates the transformative impact of the COVID-19 pandemic on these networks, with a focus on the resultant operational challenges and labor shortages experienced in Poland – a critical hub in European supply chains. The research delves into how cooperative game theory can be strategically applied to address workforce deficits, particularly in sectors vital to Poland's economy, such as food and healthcare. In the context of reduced operations triggered by illness, fatalities, and preventive measures, including travel restrictions, this study elucidates the operational dynamics within supply chain networks through game theory frameworks. It scrutinizes the strategies implemented by major corporations, including Amazon, DHL, Post Office, KFC, and McDonald's, to navigate these challenges. The methodology encompasses an analysis of the network structure of supply chain game theory, tailored to the operational confines of Poland's logistics sector, acknowledging its role as Europe's breadbasket. The findings reveal various approaches to counteract labor shortages exacerbated by the pandemic, drawing parallels with similar challenges in regions like Africa, Asia, Ukraine, Turkey, and India. The study highlights the diverse impacts of workforce disruptions on commodity prices and the revenues of logistics companies within the supply network economy. These insights contribute to a broader understanding of the financial and operational implications of cooperative game theory in the context of global health emergencies. Conclusively, this research augments existing literature by demonstrating the applicability of cooperative game theory in addressing labor shortages under pandemic-induced constraints. It presents a comprehensive analysis of the strategies employed by key players in the logistics sector, offering valuable perspectives on mitigating operational disruptions in times of crisis. ]]> Strategic Application of Cooperative Game Theory in Mitigating Labor Shortages in Post-Pandemic Logistics: A Case Study of Poland eric munyeshuri lilian kuyiena song john ayieko akoko janet awino okello killian yuh nfu doi: 10.56578/josa010406 Journal of Operational and Strategic Analytics 12-30-2023 Journal of Operational and Strategic Analytics 12-30-2023 2023 1 4 Article 214 10.56578/josa010406 https://www.acadlore.com/article/JOSA/2023_1_4/josa010406 https://www.acadlore.com/article/JOSA/2023_1_4/josa010405 In the context of the free trade port initiative, an in-depth investigation into the pricing strategies of Hainan's travel agencies was conducted, focusing on the pivotal role of customer value. This study employed empirical analytical methods, including questionnaire surveys and data analysis, to rigorously test hypotheses related to customer value-oriented pricing strategies. It was discovered that customers exhibit a predominant preference for pricing strategies anchored in their value perceptions, notwithstanding the variations in their assessments of diverse tourism products. Strategies grounded in customer value were found to be more effective in fulfilling customer requirements and augmenting satisfaction levels. The research accentuates the crucial importance of aligning pricing strategies with customer value in the context of tourism product pricing. This approach holds significant theoretical relevance and practical utility for the evolution of Hainan's tourism industry. The findings offer fresh perspectives and strategic directions for the tourism sector in Hainan, contributing to its sustainable growth and the enhancement of its competitive stature. 12-30-2023 <![CDATA[ In the context of the free trade port initiative, an in-depth investigation into the pricing strategies of Hainan's travel agencies was conducted, focusing on the pivotal role of customer value. This study employed empirical analytical methods, including questionnaire surveys and data analysis, to rigorously test hypotheses related to customer value-oriented pricing strategies. It was discovered that customers exhibit a predominant preference for pricing strategies anchored in their value perceptions, notwithstanding the variations in their assessments of diverse tourism products. Strategies grounded in customer value were found to be more effective in fulfilling customer requirements and augmenting satisfaction levels. The research accentuates the crucial importance of aligning pricing strategies with customer value in the context of tourism product pricing. This approach holds significant theoretical relevance and practical utility for the evolution of Hainan's tourism industry. The findings offer fresh perspectives and strategic directions for the tourism sector in Hainan, contributing to its sustainable growth and the enhancement of its competitive stature. ]]> Evaluation of Customer Value-Based Pricing Strategies in Hainan’s Travel Agencies under a Free Trade Port Framework shaoqing tian chaohong liu fan jiang doi: 10.56578/josa010405 Journal of Operational and Strategic Analytics 12-30-2023 Journal of Operational and Strategic Analytics 12-30-2023 2023 1 4 Article 198 10.56578/josa010405 https://www.acadlore.com/article/JOSA/2023_1_4/josa010405 https://www.acadlore.com/article/JOSA/2023_1_4/josa010404 Reducing the devastating effects of earthquakes is the main objective of planning for earthquake response. The decision-making process is essential to this attempt. However, it is particularly difficult because of the inherent uncertainties. A sophisticated methodological approach was proposed to handle these uncertainties in this study. The approach makes use of Fermatean probabilistic hesitant fuzzy sets (FePHFSs), and emphasizes the resilience of algebraic operations and their crucial role in improving the effectiveness of decision-making. In particular, a noteworthy development in the field of multiple attribute decision making (MADM) is the introduction of novel probabilistic hesitant fuzzy sets (PHFSs) aggregation operators, which are created by carefully synthesizing algebraic operations with the Combined Compromise Solution (CoCoSo) method. A key component of this technique is the application of the CoCoSo strategy, which is well known for its resilience in optimal goal selection and uses various aggregation strategies to effectively navigate the complex, multicriteria decision-making environment. A thorough numerical case study illustrates the adaptability and efficacy of this method and highlights its potential in practical settings. Decision-makers now have a new and effective tool that helps them make better informed and trustworthy decisions even in the face of uncertainty by combining PHFS with the CoCoSo technique. This method offers real-world implications for improving disaster response plans in addition to advancing the theory of decision support systems. 12-30-2023 <![CDATA[ Reducing the devastating effects of earthquakes is the main objective of planning for earthquake response. The decision-making process is essential to this attempt. However, it is particularly difficult because of the inherent uncertainties. A sophisticated methodological approach was proposed to handle these uncertainties in this study. The approach makes use of Fermatean probabilistic hesitant fuzzy sets (FePHFSs), and emphasizes the resilience of algebraic operations and their crucial role in improving the effectiveness of decision-making. In particular, a noteworthy development in the field of multiple attribute decision making (MADM) is the introduction of novel probabilistic hesitant fuzzy sets (PHFSs) aggregation operators, which are created by carefully synthesizing algebraic operations with the Combined Compromise Solution (CoCoSo) method. A key component of this technique is the application of the CoCoSo strategy, which is well known for its resilience in optimal goal selection and uses various aggregation strategies to effectively navigate the complex, multicriteria decision-making environment. A thorough numerical case study illustrates the adaptability and efficacy of this method and highlights its potential in practical settings. Decision-makers now have a new and effective tool that helps them make better informed and trustworthy decisions even in the face of uncertainty by combining PHFS with the CoCoSo technique. This method offers real-world implications for improving disaster response plans in addition to advancing the theory of decision support systems. ]]> Optimizing Earthquake Response with Fermatean Probabilistic Hesitant Fuzzy Sets: A Decision Support Framework wania iqbal tao yang shahzaib ashraf doi: 10.56578/josa010404 Journal of Operational and Strategic Analytics 12-30-2023 Journal of Operational and Strategic Analytics 12-30-2023 2023 1 4 Article 190 10.56578/josa010404 https://www.acadlore.com/article/JOSA/2023_1_4/josa010404 https://www.acadlore.com/article/JOSA/2023_1_4/josa010403 In the realm of managerial decision-making, particularly within the last few decades, the process has emerged as a formidable challenge. This paper focuses on strategic decision-making, crucial in determining organizational success or failure amidst prevailing uncertainties. To address this, the Matrix Approach to Robustness Analysis (MARA), a recent innovation, is integrated with the established Strengths-Weaknesses-Opportunities-Threats (SWOT) matrix. This integration aims to deliver robust outcomes in strategic planning for travel agencies. The methodology involves a comprehensive analysis of internal and external factors pertinent to a travel agency, applying the analytical rigor of the SWOT matrix. Subsequent to this analysis, a series of strategies are formulated. Central to this study is the identification of key environmental indicators, as perceived by stakeholders, which influence strategic outcomes. Through these indicators, various future scenarios are constructed, culminating in nineteen plausible scenarios. Each strategy, totalling twelve, is then evaluated against these scenarios to ascertain the conditions under which they are most effective, resulting in a performance matrix. The final phase involves calculating the robustness analysis scores for each strategy under two different assessment conditions: rigorous and lenient. These scores provide a basis for strategy prioritization in both scenarios. The analysis reveals that the strategy of expanding new pilgrimage tours holds the greatest promise, while the employment of relatives within the agency is deemed least effective. This study contributes to the field by offering a structured methodology for travel agencies to navigate uncertain environments, using a combination of MARA and SWOT. The findings underscore the importance of scenario-based strategic planning and robustness analysis in enhancing decision-making processes. 12-06-2023 <![CDATA[ In the realm of managerial decision-making, particularly within the last few decades, the process has emerged as a formidable challenge. This paper focuses on strategic decision-making, crucial in determining organizational success or failure amidst prevailing uncertainties. To address this, the Matrix Approach to Robustness Analysis (MARA), a recent innovation, is integrated with the established Strengths-Weaknesses-Opportunities-Threats (SWOT) matrix. This integration aims to deliver robust outcomes in strategic planning for travel agencies. The methodology involves a comprehensive analysis of internal and external factors pertinent to a travel agency, applying the analytical rigor of the SWOT matrix. Subsequent to this analysis, a series of strategies are formulated. Central to this study is the identification of key environmental indicators, as perceived by stakeholders, which influence strategic outcomes. Through these indicators, various future scenarios are constructed, culminating in nineteen plausible scenarios. Each strategy, totalling twelve, is then evaluated against these scenarios to ascertain the conditions under which they are most effective, resulting in a performance matrix. The final phase involves calculating the robustness analysis scores for each strategy under two different assessment conditions: rigorous and lenient. These scores provide a basis for strategy prioritization in both scenarios. The analysis reveals that the strategy of expanding new pilgrimage tours holds the greatest promise, while the employment of relatives within the agency is deemed least effective. This study contributes to the field by offering a structured methodology for travel agencies to navigate uncertain environments, using a combination of MARA and SWOT. The findings underscore the importance of scenario-based strategic planning and robustness analysis in enhancing decision-making processes. ]]> Strategic Adaptation in Travel Agencies: Integrating MARA with SWOT for Uncertainty Navigation mona mohebi kordsofla ali sorourkhah doi: 10.56578/josa010403 Journal of Operational and Strategic Analytics 12-06-2023 Journal of Operational and Strategic Analytics 12-06-2023 2023 1 4 Article 173 10.56578/josa010403 https://www.acadlore.com/article/JOSA/2023_1_4/josa010403 https://www.acadlore.com/article/JOSA/2023_1_4/josa010402 In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty. 12-03-2023 <![CDATA[ In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty. ]]> Interplay of Cryptocurrencies with Financial and Social Media Indicators: An Entropy-Weighted Neural-MADM Approach jéfferson augusto colombo tanzina akhter peter wanke md. abul kalam azad yong tan seyyed a. edalatpanah jorge antunes doi: 10.56578/josa010402 Journal of Operational and Strategic Analytics 12-03-2023 Journal of Operational and Strategic Analytics 12-03-2023 2023 1 4 Article 160 10.56578/josa010402 https://www.acadlore.com/article/JOSA/2023_1_4/josa010402 https://www.acadlore.com/article/JOSA/2023_1_4/josa010401 The imperatives of occupational health and safety (OHS) are increasingly recognised as critical components of business operations, particularly within logistics where manual tasks such as item picking and transportation present notable hazards. This study employs the Fine-Kinney method to conduct a risk analysis of internal transport activities in logistics systems. Hazards associated with various internal transport mediums are systematically identified and categorised. An illustrative case study involves a logistics provider based in Serbia, scrutinising the risks prevalent within warehouse operations. Through application of the Fine-Kinney method, the analysis determines the predominant risk to be collisions involving pedestrians. In response, the study advocates targeted preventive and corrective strategies to diminish these risks. Theoretical and practical contributions arise from addressing these identified risks, offering valuable insights for logistics enterprises. The emphasis on preemptive safety measures underscores their significance in safeguarding worker welfare and enhancing the efficiency of logistics operations. 11-19-2023 <![CDATA[ The imperatives of occupational health and safety (OHS) are increasingly recognised as critical components of business operations, particularly within logistics where manual tasks such as item picking and transportation present notable hazards. This study employs the Fine-Kinney method to conduct a risk analysis of internal transport activities in logistics systems. Hazards associated with various internal transport mediums are systematically identified and categorised. An illustrative case study involves a logistics provider based in Serbia, scrutinising the risks prevalent within warehouse operations. Through application of the Fine-Kinney method, the analysis determines the predominant risk to be collisions involving pedestrians. In response, the study advocates targeted preventive and corrective strategies to diminish these risks. Theoretical and practical contributions arise from addressing these identified risks, offering valuable insights for logistics enterprises. The emphasis on preemptive safety measures underscores their significance in safeguarding worker welfare and enhancing the efficiency of logistics operations. ]]> Risk Analysis in Internal Transport: An Evaluation of Occupational Health and Safety Using the Fine-Kinney Method vukašin pajić milan andrejić doi: 10.56578/josa010401 Journal of Operational and Strategic Analytics 11-19-2023 Journal of Operational and Strategic Analytics 11-19-2023 2023 1 4 Article 147 10.56578/josa010401 https://www.acadlore.com/article/JOSA/2023_1_4/josa010401 https://www.acadlore.com/article/JOSA/2023_1_3/josa010305 In the quest to reduce occupational accidents and diseases, the ergonomic performance levels of industries remain pivotal. Within this context, the metal industry in Türkiye, notorious for ergonomic challenges, was scrutinised regarding its occupational health and safety (OHS) indicators. Five pivotal criteria were employed to delineate the industry's performance: the incidence of occupational accidents, the occurrence of fatal occupational accidents, the reporting rate of occupational diseases, the cumulative days of temporary incapacity, and the overall count of insured individuals obtaining permanent incapacity benefits. A decadal period, spanning 2013-2022, served as the temporal backdrop for this examination. Utilising the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, an esteemed Multiple-Criteria Decision-Making (MCDM) technique, an assessment was conducted to ascertain the years marred by sub-optimal ergonomic performance. Notably, 2014, 2013, and 2020 were identified as the least problematic years, whereas 2022 emerged as the most critical year. This investigation underscores the imperative for strategic planning to augment ergonomic conditions in professional settings in light of OHS, particularly in recent times. 09-29-2023 <![CDATA[ <p>In the quest to reduce occupational accidents and diseases, the ergonomic performance levels of industries remain pivotal. Within this context, the metal industry in Türkiye, notorious for ergonomic challenges, was scrutinised regarding its occupational health and safety (OHS) indicators. Five pivotal criteria were employed to delineate the industry's performance: the incidence of occupational accidents, the occurrence of fatal occupational accidents, the reporting rate of occupational diseases, the cumulative days of temporary incapacity, and the overall count of insured individuals obtaining permanent incapacity benefits. A decadal period, spanning 2013-2022, served as the temporal backdrop for this examination. Utilising the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, an esteemed Multiple-Criteria Decision-Making (MCDM) technique, an assessment was conducted to ascertain the years marred by sub-optimal ergonomic performance. Notably, 2014, 2013, and 2020 were identified as the least problematic years, whereas 2022 emerged as the most critical year. This investigation underscores the imperative for strategic planning to augment ergonomic conditions in professional settings in light of OHS, particularly in recent times.</p> ]]> Ergonomic Performance Evaluation in Türkiye’s Metal Industry: Occupational Health and Safety Indicators Through VIKOR Methodology şura toptancı doi: 10.56578/josa010305 Journal of Operational and Strategic Analytics 09-29-2023 Journal of Operational and Strategic Analytics 09-29-2023 2023 1 3 Article 140 10.56578/josa010305 https://www.acadlore.com/article/JOSA/2023_1_3/josa010305 https://www.acadlore.com/article/JOSA/2023_1_3/josa010304 In the rapidly evolving industrial landscape, the decision-making process concerning which products to manufacture, their quantity, and the methods of their production has become pivotal. This study endeavors to address this need by advocating the most apt functional form of the production process for predominant manufacturing sectors. The central objective has been the maximization of output through the application of the Cobb-Douglas production function, investigated separately for both two-input and three-input scenarios. It is ascertained which of the two models exhibits greater efficacy. Subsequently, parameters of the production function are estimated utilizing advanced optimization subroutines. 09-29-2023 <![CDATA[ In the rapidly evolving industrial landscape, the decision-making process concerning which products to manufacture, their quantity, and the methods of their production has become pivotal. This study endeavors to address this need by advocating the most apt functional form of the production process for predominant manufacturing sectors. The central objective has been the maximization of output through the application of the Cobb-Douglas production function, investigated separately for both two-input and three-input scenarios. It is ascertained which of the two models exhibits greater efficacy. Subsequently, parameters of the production function are estimated utilizing advanced optimization subroutines. ]]> Production Optimization in Manufacturing Industries Using Cobb-Douglas Production Function fazal hanan rashid ali doi: 10.56578/josa010304 Journal of Operational and Strategic Analytics 09-29-2023 Journal of Operational and Strategic Analytics 09-29-2023 2023 1 3 Article 131 10.56578/josa010304 https://www.acadlore.com/article/JOSA/2023_1_3/josa010304 https://www.acadlore.com/article/JOSA/2023_1_3/josa010303 Ordering of quotients is a critical aspect of cost-efficiency problems, which hold significant interest and importance for suppliers of goods and services as well as consumers. Comparisons (ordering) are straightforward when dealing with ordinary numbers, yet in many instances, the data are imprecise, vague, or subject to seasonal variations. Consequently, such data may be unknown or derive from expert opinions. Unlike ordinary numbers, fuzzy data render quotients only partially ordered. This study examines the linear ordering of quotients with fuzzy data, expressed in terms of confidence intervals, $\alpha$-cuts, or piecewise quadratic fuzzy numbers (PQFNs), within the context of cost-efficiency problems. Moreover, the challenges associated with quotient ordering in cost-efficiency problems are introduced. 09-29-2023 <![CDATA[ Ordering of quotients is a critical aspect of cost-efficiency problems, which hold significant interest and importance for suppliers of goods and services as well as consumers. Comparisons (ordering) are straightforward when dealing with ordinary numbers, yet in many instances, the data are imprecise, vague, or subject to seasonal variations. Consequently, such data may be unknown or derive from expert opinions. Unlike ordinary numbers, fuzzy data render quotients only partially ordered. This study examines the linear ordering of quotients with fuzzy data, expressed in terms of confidence intervals, $\alpha$-cuts, or piecewise quadratic fuzzy numbers (PQFNs), within the context of cost-efficiency problems. Moreover, the challenges associated with quotient ordering in cost-efficiency problems are introduced. ]]> Addressing Cost-Efficiency Problems Based on Linear Ordering of Piecewise Quadratic Fuzzy Quotients hamiden abd el-wahed khalifa badria almaz ali yousif doi: 10.56578/josa010303 Journal of Operational and Strategic Analytics 09-29-2023 Journal of Operational and Strategic Analytics 09-29-2023 2023 1 3 Article 124 10.56578/josa010303 https://www.acadlore.com/article/JOSA/2023_1_3/josa010303 https://www.acadlore.com/article/JOSA/2023_1_3/josa010302 Investigation into the nexus between financial crises and the current account deficit within Iran’s economy was conducted, utilising time-series data spanning from 1989 to 2022. Augmented Dickey-Fuller (ADF) test validations endorsed stationarity of all variables upon first differencing. Through the deployment of Johansen's cointegration methodology, a long-term positive impact of real exchange rate oscillations on the trade deficit was discerned. Furthermore, the implementation of an error correction model (ECM) furnished additional perspectives regarding the dynamic interplay amongst the variables under consideration. The findings elucidate the repercussions of financial crises on Iran’s current account deficit, revealing a palpable influence of exchange rate volatilities on economic stability and providing insights into the nuanced macroeconomic relationships amidst periods of fiscal turmoil. The research underscores the exigency for robust fiscal and monetary strategies to navigate the intricacies of economic vulnerabilities and fortify against ensuing financial perturbations. 09-29-2023 <![CDATA[ Investigation into the nexus between financial crises and the current account deficit within Iran’s economy was conducted, utilising time-series data spanning from 1989 to 2022. Augmented Dickey-Fuller (ADF) test validations endorsed stationarity of all variables upon first differencing. Through the deployment of Johansen's cointegration methodology, a long-term positive impact of real exchange rate oscillations on the trade deficit was discerned. Furthermore, the implementation of an error correction model (ECM) furnished additional perspectives regarding the dynamic interplay amongst the variables under consideration. The findings elucidate the repercussions of financial crises on Iran’s current account deficit, revealing a palpable influence of exchange rate volatilities on economic stability and providing insights into the nuanced macroeconomic relationships amidst periods of fiscal turmoil. The research underscores the exigency for robust fiscal and monetary strategies to navigate the intricacies of economic vulnerabilities and fortify against ensuing financial perturbations. ]]> Exploring the Interface: Financial Crisis-Induced Exchange Rate Fluctuations and Implications for Iran's Current Account Deficit (1989-2022) seyed fakhrddin fakhrehosseini meysam kaviani doi: 10.56578/josa010302 Journal of Operational and Strategic Analytics 09-29-2023 Journal of Operational and Strategic Analytics 09-29-2023 2023 1 3 Article 115 10.56578/josa010302 https://www.acadlore.com/article/JOSA/2023_1_3/josa010302 https://www.acadlore.com/article/JOSA/2023_1_3/josa010301 IT, increasingly recognized as a vital contributor to competitive advantage, plays an indispensable role in augmenting business value. Effective implementation of IT Governance (ITG) mechanisms, comprising structures of responsibility, control processes, communication protocols, and decision rights, has been found to foster alignment between IT and business objectives. Such alignment is particularly critical for Small and Medium-sized Enterprises (SMEs), where the amplified business value can be realized. Yet, SMEs often grapple with challenges in implementing ITG, owing to resource constraints, communication hurdles, resistance to change, and technological complexity. The present study delves into this complex dynamic within a medium-sized industry located in southern Minas Gerais, Brazil, investigating the deployment of ITG mechanisms as a means to enhance business value through IT. An interpretivist approach characterizes the qualitative, inductive study, drawing on a case study to probe the links between ITG mechanisms, IT capabilities, and business value. Four hypotheses are put forth in the discourse, shedding light on the intricate relationships that these elements share. The findings indicate that ITG mechanisms exert a positive impact on IT business value, albeit with identifiable weaknesses and potential areas for enhancement. More effective alignment between IT and business can be achieved by addressing these shortcomings, thereby mitigating risks such as demotivation among IT professionals and resistance to change. 08-21-2023 <![CDATA[ IT, increasingly recognized as a vital contributor to competitive advantage, plays an indispensable role in augmenting business value. Effective implementation of IT Governance (ITG) mechanisms, comprising structures of responsibility, control processes, communication protocols, and decision rights, has been found to foster alignment between IT and business objectives. Such alignment is particularly critical for Small and Medium-sized Enterprises (SMEs), where the amplified business value can be realized. Yet, SMEs often grapple with challenges in implementing ITG, owing to resource constraints, communication hurdles, resistance to change, and technological complexity. The present study delves into this complex dynamic within a medium-sized industry located in southern Minas Gerais, Brazil, investigating the deployment of ITG mechanisms as a means to enhance business value through IT. An interpretivist approach characterizes the qualitative, inductive study, drawing on a case study to probe the links between ITG mechanisms, IT capabilities, and business value. Four hypotheses are put forth in the discourse, shedding light on the intricate relationships that these elements share. The findings indicate that ITG mechanisms exert a positive impact on IT business value, albeit with identifiable weaknesses and potential areas for enhancement. More effective alignment between IT and business can be achieved by addressing these shortcomings, thereby mitigating risks such as demotivation among IT professionals and resistance to change. ]]> Optimizing Business Value via It Governance Mechanisms: An Examination of SMEs in Southern Minas Gerais, Brazil ana l. m. d. maia rodrigo f. frogeri doi: 10.56578/josa010301 Journal of Operational and Strategic Analytics 08-21-2023 Journal of Operational and Strategic Analytics 08-21-2023 2023 1 3 Article 106 10.56578/josa010301 https://www.acadlore.com/article/JOSA/2023_1_3/josa010301 https://www.acadlore.com/article/JOSA/2023_1_2/josa010206 The present study scrutinizes the decision-making strategies and enhancement techniques aimed at minimizing progressive collapse in steel moment frame structures. Comparative analyses of both three-story and five-story frames were carried out, focusing on the reinforcement of external frames through the introduction of bracing. Employing ABAQUS, a sophisticated finite element software, simulations of these frames resulted in the exploration of 16 unique steel frame configurations. In an assessment of column loss impact, the middle column of the lowest story in the supporting frame was deliberately removed. Findings reveal that the axial force of the beams adjacent to the removal site in the three-story frame escalates approximately 2.15 times in relation to the values connected with corner beam extraction. Conversely, the increase in axial force of the beams adjacent to the column removal in the five-story frame varied between 5% and 49% of the respective values for beam removal conditions. Furthermore, a reduction in maximum displacement was found to correlate with an increase in the number of stories. Maximum displacements in five-story frames were observed to be roughly 7% to 22% of the corresponding values in three-story frames, with variability depending on the location of the removed column. These results indicate that the effectiveness of bracing-based reinforcement to prevent progressive collapse in steel moment frame structures intensifies with the increase in the number of stories. This performance enhancement against progressive collapse becomes particularly significant for structures comprising a higher number of stories. 06-29-2023 <![CDATA[ <p>The present study scrutinizes the decision-making strategies and enhancement techniques aimed at minimizing progressive collapse in steel moment frame structures. Comparative analyses of both three-story and five-story frames were carried out, focusing on the reinforcement of external frames through the introduction of bracing. Employing ABAQUS, a sophisticated finite element software, simulations of these frames resulted in the exploration of 16 unique steel frame configurations. In an assessment of column loss impact, the middle column of the lowest story in the supporting frame was deliberately removed. Findings reveal that the axial force of the beams adjacent to the removal site in the three-story frame escalates approximately 2.15 times in relation to the values connected with corner beam extraction. Conversely, the increase in axial force of the beams adjacent to the column removal in the five-story frame varied between 5% and 49% of the respective values for beam removal conditions. Furthermore, a reduction in maximum displacement was found to correlate with an increase in the number of stories. Maximum displacements in five-story frames were observed to be roughly 7% to 22% of the corresponding values in three-story frames, with variability depending on the location of the removed column. These results indicate that the effectiveness of bracing-based reinforcement to prevent progressive collapse in steel moment frame structures intensifies with the increase in the number of stories. This performance enhancement against progressive collapse becomes particularly significant for structures comprising a higher number of stories.</p> ]]> Analyzing and Enhancing the Resilience of Steel Moment Frame Structures Against Progressive Collapse hadi faghihmaleki fatemeh habibpour ashkan rah anjam doi: 10.56578/josa010206 Journal of Operational and Strategic Analytics 06-29-2023 Journal of Operational and Strategic Analytics 06-29-2023 2023 1 2 Article 90 10.56578/josa010206 https://www.acadlore.com/article/JOSA/2023_1_2/josa010206 https://www.acadlore.com/article/JOSA/2023_1_2/josa010205 This study emphasizes the limitations observed in the prevailing neutrosophic AHP group decision-making model. To address these limitations, an augmented neutrosophic AHP group decision-making model has been established, leveraging the potential of neutrosophic trapezoidal numbers. A comprehensive exploration of a key property of the neutrosophic trapezoidal pairwise comparison matrix is performed in this research, revealing that the current model inadequately maintains the reciprocal property of the neutrosophic trapezoidal pairwise comparison matrix. A real-world decision-making problem is resolved utilizing the introduced model, and a comparative analysis is furnished between the pre-existing neutrosophic AHP group decision-making model and the revised version. The results unequivocally demonstrate the superiority of the enhanced model. 06-29-2023 <![CDATA[ <p>This study emphasizes the limitations observed in the prevailing neutrosophic AHP group decision-making model. To address these limitations, an augmented neutrosophic AHP group decision-making model has been established, leveraging the potential of neutrosophic trapezoidal numbers. A comprehensive exploration of a key property of the neutrosophic trapezoidal pairwise comparison matrix is performed in this research, revealing that the current model inadequately maintains the reciprocal property of the neutrosophic trapezoidal pairwise comparison matrix. A real-world decision-making problem is resolved utilizing the introduced model, and a comparative analysis is furnished between the pre-existing neutrosophic AHP group decision-making model and the revised version. The results unequivocally demonstrate the superiority of the enhanced model.</p> ]]> An Enhanced AHP Group Decision-Making Model Employing Neutrosophic Trapezoidal Numbers shahid ahmad bhat doi: 10.56578/josa010205 Journal of Operational and Strategic Analytics 06-29-2023 Journal of Operational and Strategic Analytics 06-29-2023 2023 1 2 Article 81 10.56578/josa010205 https://www.acadlore.com/article/JOSA/2023_1_2/josa010205 https://www.acadlore.com/article/JOSA/2023_1_2/josa010204 Neutrosophic sets, expanded from the constructs of fuzzy and intuitionistic fuzzy sets, can accommodate degrees of truth, indeterminacy, and falsity for each element. This attribute equips them with an aptitude for a more refined interpretation of ambiguous or uncertain data. This study presents an innovative application of Neutrosophic Data Envelopment Analysis (Neu-DEA), incorporating pentagonal neutrosophic numbers in both input and output data. This novel methodology involves the transformation of traditional DEA models into a Pentagonal neutrosophic DEA model, subsequently converting it into a Crisp Linear Programming (CrLP) model. A unique ranking function is integral to this process. Performance evaluation of decision-making units (DMUs) is accomplished through the resolution of the CrLP model, with subsequent ranking of the DMUs based on their relative efficiency scores. The utility and effectiveness of this novel technique is validated through a numerical example. 06-29-2023 <![CDATA[ <p>Neutrosophic sets, expanded from the constructs of fuzzy and intuitionistic fuzzy sets, can accommodate degrees of truth, indeterminacy, and falsity for each element. This attribute equips them with an aptitude for a more refined interpretation of ambiguous or uncertain data. This study presents an innovative application of Neutrosophic Data Envelopment Analysis (Neu-DEA), incorporating pentagonal neutrosophic numbers in both input and output data. This novel methodology involves the transformation of traditional DEA models into a Pentagonal neutrosophic DEA model, subsequently converting it into a Crisp Linear Programming (CrLP) model. A unique ranking function is integral to this process. Performance evaluation of decision-making units (DMUs) is accomplished through the resolution of the CrLP model, with subsequent ranking of the DMUs based on their relative efficiency scores. The utility and effectiveness of this novel technique is validated through a numerical example.</p> ]]> Enhanced Performance Evaluation Through Neutrosophic Data Envelopment Analysis Leveraging Pentagonal Neutrosophic Numbers kshitish kumar mohanta oguz toragay doi: 10.56578/josa010204 Journal of Operational and Strategic Analytics 06-29-2023 Journal of Operational and Strategic Analytics 06-29-2023 2023 1 2 Article 70 10.56578/josa010204 https://www.acadlore.com/article/JOSA/2023_1_2/josa010204 https://www.acadlore.com/article/JOSA/2023_1_2/josa010203 This study introduces a novel approach to decision-making problems, especially in the context of hard disk selection, using the concept of the fuzzy parameterized single-valued neutrosophic soft set (FP-SVNSS). Primarily, the focus is on assigning different levels of importance to each parameter within the set, which enables a more nuanced and flexible evaluation process. This is underpinned by the development of several related concepts and the definition of basic operations such as complement, subset, union, and intersection. In the quest for clarity, the nuances of these operations and the overall framework of the FP-SVNSS method are illustrated via numerous examples. The superiority of the FP-SVNSS method over other decision-making methods is affirmed through a comprehensive comparison. The unique strength of the proposed approach lies in its ability to handle imperfect, ambiguous, and inconsistent data. Consequently, it offers greater accuracy and practicality than existing models. In the latter part of the study, the theory is put to the test by tackling a real-world decision-making problem. The selected case involves the optimal selection of hard disks, a common issue in information technology procurement. The successful application of the FP-SVNSS method to this issue provides a compelling demonstration of its potential value in practical settings. Through the exploration of this innovative decision-making methodology, this research contributes to the broader field of soft computing and decision-making theory. The findings suggest a myriad of future applications of the FP-SVNSS method in dealing with various complex and fuzzy problems in both academic and industrial contexts. 06-29-2023 <![CDATA[ <p>This study introduces a novel approach to decision-making problems, especially in the context of hard disk selection, using the concept of the fuzzy parameterized single-valued neutrosophic soft set (FP-SVNSS). Primarily, the focus is on assigning different levels of importance to each parameter within the set, which enables a more nuanced and flexible evaluation process. This is underpinned by the development of several related concepts and the definition of basic operations such as complement, subset, union, and intersection. In the quest for clarity, the nuances of these operations and the overall framework of the FP-SVNSS method are illustrated via numerous examples. The superiority of the FP-SVNSS method over other decision-making methods is affirmed through a comprehensive comparison. The unique strength of the proposed approach lies in its ability to handle imperfect, ambiguous, and inconsistent data. Consequently, it offers greater accuracy and practicality than existing models. In the latter part of the study, the theory is put to the test by tackling a real-world decision-making problem. The selected case involves the optimal selection of hard disks, a common issue in information technology procurement. The successful application of the FP-SVNSS method to this issue provides a compelling demonstration of its potential value in practical settings. Through the exploration of this innovative decision-making methodology, this research contributes to the broader field of soft computing and decision-making theory. The findings suggest a myriad of future applications of the FP-SVNSS method in dealing with various complex and fuzzy problems in both academic and industrial contexts.</p> ]]> Optimizing Hard Disk Selection via a Fuzzy Parameterized Single-Valued Neutrosophic Soft Set Approach muhammad ihsan muhammad saeed atiqe ur rahman doi: 10.56578/josa010203 Journal of Operational and Strategic Analytics 06-29-2023 Journal of Operational and Strategic Analytics 06-29-2023 2023 1 2 Article 62 10.56578/josa010203 https://www.acadlore.com/article/JOSA/2023_1_2/josa010203 https://www.acadlore.com/article/JOSA/2023_1_2/josa010202 The prevalence of decision-making methodologies catering to quantitative attributes considerably overshadows those designed for qualitative attributes. This study seeks to address this gap by extending the traditional Ideal Solutions with Constraint on Values (ISOCOV) method to a fuzzy environment, thereby enhancing its capability to handle optimal decision-making based on qualitative attributes. In this improved method, $\alpha$-cut representations are employed for managing linguistic value constraints in performance-oriented data. The proposed approach is then utilized for the selection of nanomaterials, evaluated based on five essential criteria. By subjecting the performance-based decision matrix to the modified fuzzy method, a ranking of alternatives is derived. Compared to its traditional counterpart, this fuzzy-enhanced ISOCOV method demonstrates enhanced efficiency in processing qualitative data, promising its potential compatibility and utility for decision makers dealing with performance-oriented decision-making. 06-29-2023 <![CDATA[ <p>The prevalence of decision-making methodologies catering to quantitative attributes considerably overshadows those designed for qualitative attributes. This study seeks to address this gap by extending the traditional Ideal Solutions with Constraint on Values (ISOCOV) method to a fuzzy environment, thereby enhancing its capability to handle optimal decision-making based on qualitative attributes. In this improved method, $\alpha$-cut representations are employed for managing linguistic value constraints in performance-oriented data. The proposed approach is then utilized for the selection of nanomaterials, evaluated based on five essential criteria. By subjecting the performance-based decision matrix to the modified fuzzy method, a ranking of alternatives is derived. Compared to its traditional counterpart, this fuzzy-enhanced ISOCOV method demonstrates enhanced efficiency in processing qualitative data, promising its potential compatibility and utility for decision makers dealing with performance-oriented decision-making.</p> ]]> Application of Extended Fuzzy ISOCOV Methodology in Nanomaterial Selection Based on Performance Measures nivetha martin seyyed ahmad edalatpanah doi: 10.56578/josa010202 Journal of Operational and Strategic Analytics 06-29-2023 Journal of Operational and Strategic Analytics 06-29-2023 2023 1 2 Article 55 10.56578/josa010202 https://www.acadlore.com/article/JOSA/2023_1_2/josa010202 https://www.acadlore.com/article/JOSA/2023_1_2/josa010201 The prevalent economic principle of weak disposability has been the foundation for studies in environmental assessment using Data Envelopment Analysis (DEA). Recently, a shift from classical free disposability to weak disposability has been observed as an emerging trend for treating undesirable factors in research. Weak disposability is perceived to have significant analytical power in measuring the efficiency of Decision-Making Units (DMUs). Addressing the increment of undesirable inputs, a non-radial model grounded on a non-uniform augment factor is presented. The application of this proposed model anticipates a suitable quantity for the increment of undesirable inputs. Concurrently, the model ensures a corresponding reduction in desirable inputs. Numerical instances illuminate the practicality and robustness of the proposed model and demonstrate its superior performance over its original counterpart. 06-29-2023 <![CDATA[ <p>The prevalent economic principle of weak disposability has been the foundation for studies in environmental assessment using Data Envelopment Analysis (DEA). Recently, a shift from classical free disposability to weak disposability has been observed as an emerging trend for treating undesirable factors in research. Weak disposability is perceived to have significant analytical power in measuring the efficiency of Decision-Making Units (DMUs). Addressing the increment of undesirable inputs, a non-radial model grounded on a non-uniform augment factor is presented. The application of this proposed model anticipates a suitable quantity for the increment of undesirable inputs. Concurrently, the model ensures a corresponding reduction in desirable inputs. Numerical instances illuminate the practicality and robustness of the proposed model and demonstrate its superior performance over its original counterpart.</p> ]]> Undesirable Input in Production Process: A DEA-Based Approach mahnaz maghbouli azam pourhabib yekta doi: 10.56578/josa010201 Journal of Operational and Strategic Analytics 06-29-2023 Journal of Operational and Strategic Analytics 06-29-2023 2023 1 2 Article 46 10.56578/josa010201 https://www.acadlore.com/article/JOSA/2023_1_2/josa010201 https://www.acadlore.com/article/JOSA/2023_1_1/josa010106 The growth of internet-connected services, known as the Internet of Things (IoT), has led to a proliferation of new applications. One such application is the smart home, where household appliances and devices can be remotely monitored and controlled. To achieve this, appropriate network architecture and standard protocols are used to connect various devices to the internet, resulting in an "IoT-based smart home." However, managing and regulating the entire system, as well as ensuring the security of servers and smart homes, present challenges. This paper presents an IoT architecture and discusses the issues and difficulties faced by IoT-enabled smart home systems while also proposing potential solutions. Smart homes simplify home automation tasks and offer greater convenience to users. The Industrial Wireless Sensor Network (WSN) has already demonstrated the potential of IoT, and the integration of IoT into smart homes is a logical next step. The article explores various aspects of IoT-based smart homes and highlights the need for proper management and security protocols. In conclusion, the study investigates the integration of IoT into smart homes, highlighting the challenges and solutions associated with the development of an IoT-based smart home system. The objective is to provide a framework for the development and management of IoT-based smart homes that will enhance the quality of life for users. 03-30-2023 <![CDATA[ <p>The growth of internet-connected services, known as the Internet of Things (IoT), has led to a proliferation of new applications. One such application is the smart home, where household appliances and devices can be remotely monitored and controlled. To achieve this, appropriate network architecture and standard protocols are used to connect various devices to the internet, resulting in an "IoT-based smart home." However, managing and regulating the entire system, as well as ensuring the security of servers and smart homes, present challenges. This paper presents an IoT architecture and discusses the issues and difficulties faced by IoT-enabled smart home systems while also proposing potential solutions. Smart homes simplify home automation tasks and offer greater convenience to users. The Industrial Wireless Sensor Network (WSN) has already demonstrated the potential of IoT, and the integration of IoT into smart homes is a logical next step. The article explores various aspects of IoT-based smart homes and highlights the need for proper management and security protocols. In conclusion, the study investigates the integration of IoT into smart homes, highlighting the challenges and solutions associated with the development of an IoT-based smart home system. The objective is to provide a framework for the development and management of IoT-based smart homes that will enhance the quality of life for users.</p> ]]> Investigation of IoT-Integrated Smart Homes rita de fátima muniz sheila maria muniz doi: 10.56578/josa010106 Journal of Operational and Strategic Analytics 03-30-2023 Journal of Operational and Strategic Analytics 03-30-2023 2023 1 1 Article 42 10.56578/josa010106 https://www.acadlore.com/article/JOSA/2023_1_1/josa010106 https://www.acadlore.com/article/JOSA/2023_1_1/josa010105 Effective decision-making relies on access to timely and accurate information, which is widely regarded as a valuable asset in the capital market. Accounting information is no exception, and it is critical for managers to provide such information promptly to advance their firms' economic activities. This study investigates the relationship between managers' ability and the timeliness of financial reporting, testing three research hypotheses through linear regression analysis. The statistical population comprises 115 firms listed on the Tehran Stock Exchange between 2012 and 2021, with 1150 firm-year observations. The delay in the auditor's report serves as a proxy for financial reporting timeliness. Managers' abilities are measured using Demerjian et al.'s model [1]. The findings reveal a significant, positive relationship between managerial ability and the timeliness of financial reporting, indicating that higher managerial ability is associated with lower financial reporting delay. Additionally, the results suggest that the relationship between managerial ability and financial reporting timeliness is moderated by the size of the auditing firm and the firm itself. 03-30-2023 <![CDATA[ Effective decision-making relies on access to timely and accurate information, which is widely regarded as a valuable asset in the capital market. Accounting information is no exception, and it is critical for managers to provide such information promptly to advance their firms' economic activities. This study investigates the relationship between managers' ability and the timeliness of financial reporting, testing three research hypotheses through linear regression analysis. The statistical population comprises 115 firms listed on the Tehran Stock Exchange between 2012 and 2021, with 1150 firm-year observations. The delay in the auditor's report serves as a proxy for financial reporting timeliness. Managers' abilities are measured using Demerjian et al.'s model [1]. The findings reveal a significant, positive relationship between managerial ability and the timeliness of financial reporting, indicating that higher managerial ability is associated with lower financial reporting delay. Additionally, the results suggest that the relationship between managerial ability and financial reporting timeliness is moderated by the size of the auditing firm and the firm itself. ]]> The Effect of Managerial Ability on the Timeliness of Financial Reporting: The Role of Audit Firm and Company Size azadeh shemshad ramin goudarzi karim doi: 10.56578/josa010105 Journal of Operational and Strategic Analytics 03-30-2023 Journal of Operational and Strategic Analytics 03-30-2023 2023 1 1 Article 34 10.56578/josa010105 https://www.acadlore.com/article/JOSA/2023_1_1/josa010105 https://www.acadlore.com/article/JOSA/2023_1_1/josa010104 In today's rapidly changing economic environment, the success of organizations is largely determined by their organizational efficiency. The impact of innovation and strategic planning on organizational performance is the focus of this study, which was conducted in 2022 among middle-level managers and employees of public and private sector hospitals. A total of 63 questionnaires were collected, resulting in a response rate of approximately 72.41%. Structural equation modeling with Smart PLS3 software was utilized to examine the relationships between the variables. The results indicate that organizational performance is positively impacted by innovation. Furthermore, the study found that the performance of organizations is positively influenced by strategic planning. These findings have significant implications for managers and decision-makers in the healthcare sector and can inform the development of effective strategies for improving organizational performance. 03-30-2023 <![CDATA[ In today's rapidly changing economic environment, the success of organizations is largely determined by their organizational efficiency. The impact of innovation and strategic planning on organizational performance is the focus of this study, which was conducted in 2022 among middle-level managers and employees of public and private sector hospitals. A total of 63 questionnaires were collected, resulting in a response rate of approximately 72.41%. Structural equation modeling with Smart PLS3 software was utilized to examine the relationships between the variables. The results indicate that organizational performance is positively impacted by innovation. Furthermore, the study found that the performance of organizations is positively influenced by strategic planning. These findings have significant implications for managers and decision-makers in the healthcare sector and can inform the development of effective strategies for improving organizational performance. ]]> Does Organizational Performance Increase with Innovation and Strategic Planning? natalja osintsev bardia khalilian doi: 10.56578/josa010104 Journal of Operational and Strategic Analytics 03-30-2023 Journal of Operational and Strategic Analytics 03-30-2023 2023 1 1 Article 25 10.56578/josa010104 https://www.acadlore.com/article/JOSA/2023_1_1/josa010104 https://www.acadlore.com/article/JOSA/2023_1_1/josa010103 A bibliometric analysis is presented in this paper to examine the use of Data Envelopment Analysis (DEA) in the domain of Supply Chain Management (SCM). The research trends on DEA in SCM from 2000 to 2023 are explored, using data obtained from the Web of Science database (WoS) and VOS viewer software for detailed mapping of the articles. The numerous articles that use DEA in SCM worldwide are analyzed and summarized in this bibliometric study, producing a complete assessment of DEA in the field from 352 academic papers published in high-ranking publications. The articles are classified according to the year of publication, countries of the author(s), working areas, journals, and content of studies. Based on the findings of this research, tremendous potential is shown for DEA as a suitable evaluation instrument for future studies on sustainability concerns in SCM. 03-30-2023 <![CDATA[ A bibliometric analysis is presented in this paper to examine the use of Data Envelopment Analysis (DEA) in the domain of Supply Chain Management (SCM). The research trends on DEA in SCM from 2000 to 2023 are explored, using data obtained from the Web of Science database (WoS) and VOS viewer software for detailed mapping of the articles. The numerous articles that use DEA in SCM worldwide are analyzed and summarized in this bibliometric study, producing a complete assessment of DEA in the field from 352 academic papers published in high-ranking publications. The articles are classified according to the year of publication, countries of the author(s), working areas, journals, and content of studies. Based on the findings of this research, tremendous potential is shown for DEA as a suitable evaluation instrument for future studies on sustainability concerns in SCM. ]]> Bibliometric Analysis of Data Envelopment Analysis in Supply Chain Management cigdem sıcakyuz doi: 10.56578/josa010103 Journal of Operational and Strategic Analytics 03-30-2023 Journal of Operational and Strategic Analytics 03-30-2023 2023 1 1 Article 14 10.56578/josa010103 https://www.acadlore.com/article/JOSA/2023_1_1/josa010103 https://www.acadlore.com/article/JOSA/2023_1_1/josa010102 Linear programming problems (LPP) have been widely used to address real-world problems, including the stock portfolio problem. In this study, an approach is proposed that incorporates Pythagorean fuzzy numbers (PFN) in the rate of risked return, portfolio risk amount, and expected return rate. The problem is transformed into a deterministic form using the scoring function, and a solution algorithm is being developed to provide portfolio investment choices. One of the key features of this study is the investor's ability to choose risk coefficients to increase expected returns and set their circumstances while determining their strategies. The optimum return rate is identified using the TORA program. An example is provided to demonstrate the efficiency and reliability of the method. 03-30-2023 <![CDATA[ Linear programming problems (LPP) have been widely used to address real-world problems, including the stock portfolio problem. In this study, an approach is proposed that incorporates Pythagorean fuzzy numbers (PFN) in the rate of risked return, portfolio risk amount, and expected return rate. The problem is transformed into a deterministic form using the scoring function, and a solution algorithm is being developed to provide portfolio investment choices. One of the key features of this study is the investor's ability to choose risk coefficients to increase expected returns and set their circumstances while determining their strategies. The optimum return rate is identified using the TORA program. An example is provided to demonstrate the efficiency and reliability of the method. ]]> Stock Portfolio Optimization Using Pythagorean Fuzzy Numbers salwa el-morsy doi: 10.56578/josa010102 Journal of Operational and Strategic Analytics 03-30-2023 Journal of Operational and Strategic Analytics 03-30-2023 2023 1 1 Article 8 10.56578/josa010102 https://www.acadlore.com/article/JOSA/2023_1_1/josa010102 https://www.acadlore.com/article/JOSA/2023_1_1/josa010101 The purpose of this empirical study is to evaluate and explain the fiscal performance of Indian states from 2009-10 to 2014-15 using a network DEA approach. While previous research has compared India's fiscal and developmental performance at the sub-national level, this study departs from the extant literature by evaluating state-wise performance at a disaggregated level. The states are first compared based on their tax mobilization and then evaluated in terms of development spending and overall financial performance. Censored regression analysis is also used to explore the impact of outstanding liabilities on GDP ratio, Gross Capital Formation, and GDP growth rate. The results indicate a positive association between efficiency scores and GDP growth rate and log of Gross Capital Formation. However, the linkage between efficiency and the outstanding liabilities ratio is negative. These findings suggest the need for a balanced approach to government spending to avoid the recurrence of the debt crisis in the future. 03-30-2023 <![CDATA[ <p style="text-align: justify">The purpose of this empirical study is to evaluate and explain the fiscal performance of Indian states from 2009-10 to 2014-15 using a network DEA approach. While previous research has compared India's fiscal and developmental performance at the sub-national level, this study departs from the extant literature by evaluating state-wise performance at a disaggregated level. The states are first compared based on their tax mobilization and then evaluated in terms of development spending and overall financial performance. Censored regression analysis is also used to explore the impact of outstanding liabilities on GDP ratio, Gross Capital Formation, and GDP growth rate. The results indicate a positive association between efficiency scores and GDP growth rate and log of Gross Capital Formation. However, the linkage between efficiency and the outstanding liabilities ratio is negative. These findings suggest the need for a balanced approach to government spending to avoid the recurrence of the debt crisis in the future.</p> ]]> Efficiency and Fiscal Performance of Indian States: An Empirical Analysis Using Network DEA ram pratap sinha seyyed ahmad edalatpanah doi: 10.56578/josa010101 Journal of Operational and Strategic Analytics 03-30-2023 Journal of Operational and Strategic Analytics 03-30-2023 2023 1 1 Article 1 10.56578/josa010101 https://www.acadlore.com/article/JOSA/2023_1_1/josa010101