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Journal of Operational and Strategic Analytics
JISC
Journal of Operational and Strategic Analytics (JOSA)
JOTE
ISSN (print): 2959-0094
ISSN (online): 2959-0108
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2024: Vol. 2
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Journal of Operational and Strategic Analytics (JOSA) is a distinguished academic journal, specializing in operational and strategic analytics. It uniquely bridges rigorous academic research with practical applications, making it a valuable resource for both scholars and industry practitioners. JOSA covers a diverse range of topics, including data analysis, strategic decision-making processes, and the application of analytics in various organizational contexts. The journal is particularly noted for its exploration of how emerging technologies like artificial intelligence and machine learning are influencing strategic analytics. This focus not only provides insights into current trends but also into future challenges and opportunities in the field. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief(1)
seyyed ahmad edalatpanah
Ayandegan Institute of Higher Education, Iran
s.a.edalatpanah@aihe.ac.ir | website
Research interests: Mathematical Programming; Operational Research; Numerical Modeling; Strategic Analytics; Decision Support Systems; Uncertainty Theories; Soft Computing

Aims & Scope

Aims

Journal of Operational and Strategic Analytics (JOSA) is a premier open-access journal that specializes in data-driven analysis and decision-making in the fields of statistics, computer programming, and operations research. Its mission is to enhance theoretical and practical understanding of problem-solving in various contexts, including individual, business, organizational, governmental, and societal levels. JOSA welcomes diverse submissions like reviews, research papers, and short communications, including Special Issues on specific topics. The journal is distinct for its comprehensive approach, offering insights into the application of analytics across various organizations and entities.

JOSA encourages in-depth publication of both theoretical and experimental results, with no constraints on paper length to ensure detailed and reproducible findings. Unique features of the journal include:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

JOSA offers a broad and comprehensive scope that distinctively positions it within the academic community, encompassing a wide array of topics in operational and strategic analytics:

  • Operational and Strategic Analysis: Exploring advanced methodologies in operational research and strategic analytics, focusing on optimizing business processes and decision-making strategies.

  • Strategic Planning and Management: Covering aspects of strategic planning, including formulation, implementation, and evaluation within various organizational contexts.

  • Business Analytics and Management: Delving into the application of analytics in business management, including performance analysis, market trends, and consumer behavior.

  • Problem Structuring Methods (PSMs): Investigating methodologies for identifying, structuring, and analyzing complex decision-making scenarios in organizational settings.

  • Knowledge and Information Management: Focusing on effective management of knowledge resources and information systems in organizations to enhance decision-making and strategic planning.

  • Decision Analytics and Systems: Examining systems and tools designed to improve the quality and efficiency of decision-making processes in businesses and organizations.

  • Data-Driven Analysis: Emphasizing the role of data in quantitative and qualitative analysis to guide operational and strategic decisions.

  • Digitalizing and Emerging Technologies: Exploring the impact of digital transformation and emerging technologies like AI, IoT, and blockchain on operational and strategic planning.

  • Accounting and Quantitative Finance: Applying quantitative methods to accounting and financial decision-making, including risk assessment, investment analysis, and financial modeling.

  • Health and Tourism Management: Investigating the application of operational and strategic analytics in the health and tourism sectors, focusing on improving service delivery and customer satisfaction.

  • Project and Risk Management: Covering the principles and practices of effective project management and risk mitigation strategies in various industries.

  • Complexity and Uncertainty Management: Addressing methods for managing complexity and uncertainty in business operations and strategic planning.

  • Performance Efficiency: Analyzing strategies and tools for enhancing organizational performance, productivity, and operational efficiency.

  • Innovative Applications of Decision Science: Showcasing novel applications of decision science in emerging areas and new themes, expanding the boundaries of operational and strategic analytics.

Articles
Recent Articles
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Open Access
Research article
Optimizing Decision-Making Through Customer-Centric Market Basket Analysis
md jiabul hoque ,
md. saiful islam ,
syed abrar mohtasim
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Available online: 04-29-2024

Abstract

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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.

Abstract

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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.

Abstract

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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.

Abstract

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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.

Abstract

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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.

Abstract

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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.

Abstract

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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.
Open Access
Research article
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
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Available online: 12-30-2023

Abstract

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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.

Abstract

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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.

Abstract

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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.

Abstract

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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.
Open Access
Research article
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
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Available online: 12-03-2023

Abstract

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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.
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