Latest open access articles published in Journal of Intelligent Sustainability and Decision Analytics at https://www.acadlore.com/journals/JISDA https://www.acadlore.com/journals/JISDA Acadlore en Creative Commons Attribution(CC - BY) JISDA support@acadlore.com https://www.acadlore.com/article/JISDA/2026_1_2/jisda010205 Reliable and sustainable electricity supply remains a critical challenge for rural healthcare facilities, where energy planning involves competing technical, economic, environmental, social, and resilience considerations under substantial uncertainty. Conventional multi-criteria decision models may have difficulty representing linguistic assessments, hesitation, and the hierarchical structure of subdivided evaluation attributes. This study develops a sustainability-oriented decision-support framework for resilient microgrid selection by integrating linguistic q-rung orthopair fuzzy hypersoft sets (Lq-ROFHSs) with an extended technique for order preference by similarity to ideal solution (TOPSIS) model. Correlation and weighted correlation coefficients for Lq-ROFHSs were formulated and their mathematical properties were examined. These measures were then incorporated into a multi-attribute group decision-making (MAGDM) procedure in which expert assessments, attribute weights, and subdivided criteria were represented within a unified linguistic fuzzy structure. The framework was applied to the selection of microgrid alternatives for rural clinics across cost efficiency, reliability, environmental benefit, and scalability-related criteria. The analysis produced a clear preference ordering among the evaluated alternatives, while comparison with existing decision-making approaches showed consistent ranking behaviour and demonstrated the applicability of the proposed formulation to structured decisions under linguistic uncertainty. The results indicate that combining hypersoft attribute representation with correlation-based TOPSIS provides a transparent framework for sustainability-oriented evaluation involving complex and imprecise information. The proposed approach offers a reproducible decision-analytic basis for microgrid planning and can be extended to other sustainability and energy-system decisions involving multiple stakeholders, competing criteria, and uncertain assessments. 06-29-2026 <![CDATA[ Reliable and sustainable electricity supply remains a critical challenge for rural healthcare facilities, where energy planning involves competing technical, economic, environmental, social, and resilience considerations under substantial uncertainty. Conventional multi-criteria decision models may have difficulty representing linguistic assessments, hesitation, and the hierarchical structure of subdivided evaluation attributes. This study develops a sustainability-oriented decision-support framework for resilient microgrid selection by integrating linguistic q-rung orthopair fuzzy hypersoft sets (Lq-ROFHSs) with an extended technique for order preference by similarity to ideal solution (TOPSIS) model. Correlation and weighted correlation coefficients for Lq-ROFHSs were formulated and their mathematical properties were examined. These measures were then incorporated into a multi-attribute group decision-making (MAGDM) procedure in which expert assessments, attribute weights, and subdivided criteria were represented within a unified linguistic fuzzy structure. The framework was applied to the selection of microgrid alternatives for rural clinics across cost efficiency, reliability, environmental benefit, and scalability-related criteria. The analysis produced a clear preference ordering among the evaluated alternatives, while comparison with existing decision-making approaches showed consistent ranking behaviour and demonstrated the applicability of the proposed formulation to structured decisions under linguistic uncertainty. The results indicate that combining hypersoft attribute representation with correlation-based TOPSIS provides a transparent framework for sustainability-oriented evaluation involving complex and imprecise information. The proposed approach offers a reproducible decision-analytic basis for microgrid planning and can be extended to other sustainability and energy-system decisions involving multiple stakeholders, competing criteria, and uncertain assessments. ]]> Sustainability-Oriented Microgrid Selection for Rural Clinics Using Linguistic q-Rung Orthopair Fuzzy Hypersoft Technique for Order Preference by Similarity to Ideal Solution muhammad saqlain jose m. merigo doi: 10.56578/jisda010205 Journal of Intelligent Sustainability and Decision Analytics 06-29-2026 Journal of Intelligent Sustainability and Decision Analytics 06-29-2026 2026 1 2 Article 178 10.56578/jisda010205 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010205 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010204 Supply chain finance can facilitate access to working capital by leveraging the creditworthiness of focal buyers to improve financing conditions for suppliers, an advantage that is particularly relevant in emerging economies characterized by constrained access to credit. However, existing supply chain finance readiness assessments remain predominantly focused on economic and financial considerations and have largely been developed for mature receivables-financing environments, thereby providing insufficient consideration of the sustainability and organizational conditions encountered in emerging-economy manufacturing. To address this limitation, a hybrid multi-criteria decision-making framework was developed to assess sustainable supply chain finance readiness based on 21 sub-criteria grouped into six sustainability and organizational criteria. The Criteria Importance Through Intercriteria Correlation (CRITIC) method was applied to obtain data-driven sub-criteria weights from assessments provided by 11 industry participants, thereby reducing reliance on explicitly elicited subjective preference weights. Fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) and grey relational analysis were subsequently employed to prioritize the six criteria. Consistent ranking patterns were obtained using both methods. Social factors were ranked highest, with scores of 0.566 under fuzzy TOPSIS and 0.695 under the grey relational analysis, whereas organizational policies received the lowest scores, at 0.236 and 0.454, respectively. The convergence of the two ranking approaches indicates that sustainable supply chain finance readiness in emerging-economy manufacturing is shaped not only by firm-level financial and operational capacity but also by broader relational and institutional conditions. Accordingly, greater sustainable supply chain finance readiness may be supported by strengthening inter-organizational coordination, stakeholder relationships, and institutional support while simultaneously developing firm-level capabilities. The proposed framework provides a structured basis for identifying priority dimensions of sustainable supply chain finance readiness and may support more context-sensitive financing and sustainability strategies in emerging-economy manufacturing supply chains. 06-20-2026 <![CDATA[ Supply chain finance can facilitate access to working capital by leveraging the creditworthiness of focal buyers to improve financing conditions for suppliers, an advantage that is particularly relevant in emerging economies characterized by constrained access to credit. However, existing supply chain finance readiness assessments remain predominantly focused on economic and financial considerations and have largely been developed for mature receivables-financing environments, thereby providing insufficient consideration of the sustainability and organizational conditions encountered in emerging-economy manufacturing. To address this limitation, a hybrid multi-criteria decision-making framework was developed to assess sustainable supply chain finance readiness based on 21 sub-criteria grouped into six sustainability and organizational criteria. The Criteria Importance Through Intercriteria Correlation (CRITIC) method was applied to obtain data-driven sub-criteria weights from assessments provided by 11 industry participants, thereby reducing reliance on explicitly elicited subjective preference weights. Fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) and grey relational analysis were subsequently employed to prioritize the six criteria. Consistent ranking patterns were obtained using both methods. Social factors were ranked highest, with scores of 0.566 under fuzzy TOPSIS and 0.695 under the grey relational analysis, whereas organizational policies received the lowest scores, at 0.236 and 0.454, respectively. The convergence of the two ranking approaches indicates that sustainable supply chain finance readiness in emerging-economy manufacturing is shaped not only by firm-level financial and operational capacity but also by broader relational and institutional conditions. Accordingly, greater sustainable supply chain finance readiness may be supported by strengthening inter-organizational coordination, stakeholder relationships, and institutional support while simultaneously developing firm-level capabilities. The proposed framework provides a structured basis for identifying priority dimensions of sustainable supply chain finance readiness and may support more context-sensitive financing and sustainability strategies in emerging-economy manufacturing supply chains. ]]> Sustainable Supply Chain Finance Readiness in Emerging-Economy Manufacturing: A Hybrid Multi-Criteria Decision-Making Approach syed md sadman rafid md farhan tahmid hossain shah md. ashiquzzaman nipu doi: 10.56578/jisda010204 Journal of Intelligent Sustainability and Decision Analytics 06-20-2026 Journal of Intelligent Sustainability and Decision Analytics 06-20-2026 2026 1 2 Article 162 10.56578/jisda010204 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010204 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010203 Supplier selection has emerged as a critical strategic decision affecting both supply chain performance and long-term sustainability outcomes. Traditional selection methods have typically emphasized economic factors, often neglecting environmental and social considerations. This study develops an integrated analytic network process (ANP) and Višekriterijumska optimizacija i kompromisno rešenje (VIKOR; Multi-Criteria Optimization and Compromise Solution) framework for prioritizing sustainability indicators and selecting sustainable glass suppliers in Iran. Indicators were identified through literature review and expert consultation, structured across economic, environmental, and social dimensions. ANP determined indicator weights while accounting for interdependencies among criteria. These weights were then incorporated into VIKOR to rank suppliers and identify compromise solutions under conflicting objectives. The parameter v represented the emphasis on maximum group utility versus minimum individual regret. Results identified Qazvin Glass Company and Ardakan Yazd Glass Company as the top-performing suppliers. Sensitivity analysis confirmed ranking stability across parameter variations. By integrating interdependent sustainability indicators with compromise-based supplier ranking, the proposed ANP-VIKOR framework provides a structured decision-support approach through which economic, environmental, and social considerations can be incorporated simultaneously into supplier selection. The proposed framework offers a structured approach for integrating interdependent sustainability indicators into supplier selection and provides a transferable methodology for other manufacturing industries. 06-09-2026 <![CDATA[ Supplier selection has emerged as a critical strategic decision affecting both supply chain performance and long-term sustainability outcomes. Traditional selection methods have typically emphasized economic factors, often neglecting environmental and social considerations. This study develops an integrated analytic network process (ANP) and Višekriterijumska optimizacija i kompromisno rešenje (VIKOR; Multi-Criteria Optimization and Compromise Solution) framework for prioritizing sustainability indicators and selecting sustainable glass suppliers in Iran. Indicators were identified through literature review and expert consultation, structured across economic, environmental, and social dimensions. ANP determined indicator weights while accounting for interdependencies among criteria. These weights were then incorporated into VIKOR to rank suppliers and identify compromise solutions under conflicting objectives. The parameter v represented the emphasis on maximum group utility versus minimum individual regret. Results identified Qazvin Glass Company and Ardakan Yazd Glass Company as the top-performing suppliers. Sensitivity analysis confirmed ranking stability across parameter variations. By integrating interdependent sustainability indicators with compromise-based supplier ranking, the proposed ANP-VIKOR framework provides a structured decision-support approach through which economic, environmental, and social considerations can be incorporated simultaneously into supplier selection. The proposed framework offers a structured approach for integrating interdependent sustainability indicators into supplier selection and provides a transferable methodology for other manufacturing industries. ]]> An Integrated Analytic Network Process-VIKOR Framework for Sustainability Indicator Prioritization and Glass Supplier Selection samira baratian hamed fazlollahtabar nasim ganjavi doi: 10.56578/jisda010203 Journal of Intelligent Sustainability and Decision Analytics 06-09-2026 Journal of Intelligent Sustainability and Decision Analytics 06-09-2026 2026 1 2 Article 147 10.56578/jisda010203 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010203 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010202 Effective ventilation is essential for maintaining suitable airflow and thermal conditions in controlled-environment vertical hydroponic systems. However, ventilation performance is strongly influenced by the number and arrangement of inlet and exhaust fans, and maximizing airflow velocity alone may not provide an optimal operating environment. In this study, an integrated computational fluid dynamics (CFD) and multi-criteria decision-making framework was developed to optimize inlet–exhaust fan configurations in a vertical hydroponic system accommodating 18 leafy vegetable plants. Ten ventilation configurations were evaluated using three-dimensional CFD simulations, with pressure, airflow velocity, temperature, and computational time considered as performance criteria. Across the simulated configurations, computational times ranged from approximately 160 to 190 s, stabilized temperatures remained near 293 K, airflow velocities ranged from 3.56 to 11.26 m/s, and pressures ranged from 101,325 to 101,408 Pa. The simulated configurations were subsequently evaluated using grey relational analysis, with criterion weights determined using the analytic hierarchy process (AHP). Sensitivity analysis was additionally performed. Under the selected weighting scheme, Configuration 1, comprising one inlet fan and one exhaust fan, achieved the highest grey relational grade and was therefore identified as the preferred ventilation configuration. The results indicate that ventilation effectiveness is governed by the combined performance of airflow and thermal characteristics rather than by maximum airflow velocity alone. Accordingly, increasing the number of fans does not necessarily improve overall ventilation performance. The proposed CFD–AHP–grey relational analysis framework provides a systematic basis for selecting ventilation configurations in vertical hydroponic systems and may support the development of more thermally stable and operationally efficient controlled-environment cultivation systems. 06-04-2026 <![CDATA[ Effective ventilation is essential for maintaining suitable airflow and thermal conditions in controlled-environment vertical hydroponic systems. However, ventilation performance is strongly influenced by the number and arrangement of inlet and exhaust fans, and maximizing airflow velocity alone may not provide an optimal operating environment. In this study, an integrated computational fluid dynamics (CFD) and multi-criteria decision-making framework was developed to optimize inlet–exhaust fan configurations in a vertical hydroponic system accommodating 18 leafy vegetable plants. Ten ventilation configurations were evaluated using three-dimensional CFD simulations, with pressure, airflow velocity, temperature, and computational time considered as performance criteria. Across the simulated configurations, computational times ranged from approximately 160 to 190 s, stabilized temperatures remained near 293 K, airflow velocities ranged from 3.56 to 11.26 m/s, and pressures ranged from 101,325 to 101,408 Pa. The simulated configurations were subsequently evaluated using grey relational analysis, with criterion weights determined using the analytic hierarchy process (AHP). Sensitivity analysis was additionally performed. Under the selected weighting scheme, Configuration 1, comprising one inlet fan and one exhaust fan, achieved the highest grey relational grade and was therefore identified as the preferred ventilation configuration. The results indicate that ventilation effectiveness is governed by the combined performance of airflow and thermal characteristics rather than by maximum airflow velocity alone. Accordingly, increasing the number of fans does not necessarily improve overall ventilation performance. The proposed CFD–AHP–grey relational analysis framework provides a systematic basis for selecting ventilation configurations in vertical hydroponic systems and may support the development of more thermally stable and operationally efficient controlled-environment cultivation systems. ]]> Optimization of Inlet–Exhaust Fan Configurations for Vertical Hydroponics Using Computational Fluid Dynamics and Grey Relational Analysis muhammad atif saeed doi: 10.56578/jisda010202 Journal of Intelligent Sustainability and Decision Analytics 06-04-2026 Journal of Intelligent Sustainability and Decision Analytics 06-04-2026 2026 1 2 Article 130 10.56578/jisda010202 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010202 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010201 Selecting suitable ash lagoon cells is a critical operational and environmental challenge in coal-fired power plant ash disposal systems. Conventional selection methods often emphasize disposal capacity while overlooking hydraulic connectivity, recirculation performance, operational flexibility, and infrastructure interdependencies. This study proposes an integrated Multi-Criteria Decision-Making (MCDM) framework combining the Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to prioritize feasible ash lagoon cells at the Poplar River Power Station (PRPS) under baseline and post-project conditions. Five criteria were evaluated: remaining capacity, hydraulic connectivity, discharge accessibility, recirculation impact, and operational flexibility. AHP was initially applied to assess judgment consistency, followed by FAHP to determine criteria weights under uncertainty. TOPSIS was then used to rank three lagoon-cell alternatives, and sensitivity analysis was conducted to assess robustness. Results identified remaining capacity as the most influential criterion. Cell 4E consistently ranked highest due to its balanced disposal capacity and operational performance, while the planned expansion of Cell 5 improved its post-project ranking. Sensitivity analysis confirmed ranking stability, demonstrating the robustness and practical applicability of the proposed FAHP–TOPSIS framework for infrastructure decision-making under uncertainty. 05-29-2026 <![CDATA[ <p>Selecting suitable ash lagoon cells is a critical operational and environmental challenge in coal-fired power plant ash disposal systems. Conventional selection methods often emphasize disposal capacity while overlooking hydraulic connectivity, recirculation performance, operational flexibility, and infrastructure interdependencies. This study proposes an integrated Multi-Criteria Decision-Making (MCDM) framework combining the Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to prioritize feasible ash lagoon cells at the Poplar River Power Station (PRPS) under baseline and post-project conditions. Five criteria were evaluated: remaining capacity, hydraulic connectivity, discharge accessibility, recirculation impact, and operational flexibility. AHP was initially applied to assess judgment consistency, followed by FAHP to determine criteria weights under uncertainty. TOPSIS was then used to rank three lagoon-cell alternatives, and sensitivity analysis was conducted to assess robustness. Results identified remaining capacity as the most influential criterion. Cell 4E consistently ranked highest due to its balanced disposal capacity and operational performance, while the planned expansion of Cell 5 improved its post-project ranking. Sensitivity analysis confirmed ranking stability, demonstrating the robustness and practical applicability of the proposed FAHP–TOPSIS framework for infrastructure decision-making under uncertainty.</p> ]]> Lagoon-Cell Prioritization in Closed-Loop Ash Disposal Systems Using an Integrated Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution Framework sarmad haider golam kabir seyedmehdi mirmohammadsadeghi doi: 10.56578/jisda010201 Journal of Intelligent Sustainability and Decision Analytics 05-29-2026 Journal of Intelligent Sustainability and Decision Analytics 05-29-2026 2026 1 2 Article 114 10.56578/jisda010201 https://www.acadlore.com/article/JISDA/2026_1_2/jisda010201 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010105 Pharmaceutical cold chain warehousing (PCCW) systems operate in highly regulated environments where maintaining product integrity and ensuring continuous operation are critical. In recent years, increasing exposure to systemic disruptions has made it necessary to reconsider how sustainability and resilience criteria are prioritised in warehouse configuration and management. This study aims to investigate how the relative influence of decision criteria evolves under different disruption conditions and to develop a structured analytical framework for evaluating such changes. A decision-analytic framework based on the Decision Criteria Influence (DCI) model was developed. The framework integrated a dual-dimension evaluation of sustainability performance and system reliability with a scenario-based sensitivity adjustment. A structured assessment was conducted across three representative disruption contexts, including energy supply instability, pandemic-induced demand fluctuations, and war-related systemic disruptions. The results showed that under stable conditions, sustainability-oriented criteria, particularly energy efficiency and monitoring-related factors, exerted dominant influence. However, as disruption intensity increased, criteria associated with infrastructure redundancy, inventory buffering capacity, and system reliability became progressively more significant. In extreme scenarios, such as war-related disruptions, resilience-oriented determinants clearly dominated the decision structure, indicating a substantial reordering of strategic priorities. The findings indicate that decision criteria in pharmaceutical cold chain systems exhibit strong context dependency and cannot be treated as static evaluation factors. The proposed framework provides a structured decision-analytic approach for capturing dynamic priority shifts under uncertainty and offers methodological support for designing adaptive and resilient cold chain infrastructures. 03-30-2026 <![CDATA[ Pharmaceutical cold chain warehousing (PCCW) systems operate in highly regulated environments where maintaining product integrity and ensuring continuous operation are critical. In recent years, increasing exposure to systemic disruptions has made it necessary to reconsider how sustainability and resilience criteria are prioritised in warehouse configuration and management. This study aims to investigate how the relative influence of decision criteria evolves under different disruption conditions and to develop a structured analytical framework for evaluating such changes. A decision-analytic framework based on the Decision Criteria Influence (DCI) model was developed. The framework integrated a dual-dimension evaluation of sustainability performance and system reliability with a scenario-based sensitivity adjustment. A structured assessment was conducted across three representative disruption contexts, including energy supply instability, pandemic-induced demand fluctuations, and war-related systemic disruptions. The results showed that under stable conditions, sustainability-oriented criteria, particularly energy efficiency and monitoring-related factors, exerted dominant influence. However, as disruption intensity increased, criteria associated with infrastructure redundancy, inventory buffering capacity, and system reliability became progressively more significant. In extreme scenarios, such as war-related disruptions, resilience-oriented determinants clearly dominated the decision structure, indicating a substantial reordering of strategic priorities. The findings indicate that decision criteria in pharmaceutical cold chain systems exhibit strong context dependency and cannot be treated as static evaluation factors. The proposed framework provides a structured decision-analytic approach for capturing dynamic priority shifts under uncertainty and offers methodological support for designing adaptive and resilient cold chain infrastructures. ]]> Dynamic Evaluation of Decision Criteria in Pharmaceutical Cold Chain Warehousing: A Decision-Analytic Framework under Disruption Risk svetlana dabić-miletić doi: 10.56578/jisda010105 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 2026 1 1 Article 100 10.56578/jisda010105 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010105 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010104 Rapid expansion of the construction industry in Bangladesh has been accompanied by substantial contributions to economic development, while simultaneously intensifying environmental pressures. In response to these challenges, the adoption of circular economy principles has been widely recognized as a viable pathway toward sustainable development. However, despite growing global attention, the effective implementation of a circular economy within the construction industry of emerging economies remains limited and insufficiently structured. In this study, the key enablers facilitating circular economy implementation in the construction industry of an emerging economy were systematically identified and analyzed. Initially, a comprehensive set of enablers was derived through an extensive literature review and subsequently refined through expert validation to ensure contextual relevance. The total interpretive structural modeling methodology was then employed to develop an interpretive structural model, through which hierarchical relationships and contextual interdependencies among the identified enablers were established. The robustness and practical applicability of the proposed model were further validated through expert assessment. In addition, Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was conducted to classify the enablers based on their driving power and dependence. The results revealed a six-level hierarchical structure, in which “government support and policy framework,” “top management commitment,” and “advanced knowledge and awareness of the circular economy” were identified as dominant driving enablers exerting significant influence over other factors. These findings provide a comprehensive and structured understanding of the systemic interactions among circular economy enablers and offer actionable insights for policymakers and industry practitioners in emerging economies. The study contributes to the existing body of knowledge by advancing a theoretically grounded and empirically validated framework that supports strategic prioritization and facilitates the transition toward a circular construction paradigm. 03-30-2026 <![CDATA[ Rapid expansion of the construction industry in Bangladesh has been accompanied by substantial contributions to economic development, while simultaneously intensifying environmental pressures. In response to these challenges, the adoption of circular economy principles has been widely recognized as a viable pathway toward sustainable development. However, despite growing global attention, the effective implementation of a circular economy within the construction industry of emerging economies remains limited and insufficiently structured. In this study, the key enablers facilitating circular economy implementation in the construction industry of an emerging economy were systematically identified and analyzed. Initially, a comprehensive set of enablers was derived through an extensive literature review and subsequently refined through expert validation to ensure contextual relevance. The total interpretive structural modeling methodology was then employed to develop an interpretive structural model, through which hierarchical relationships and contextual interdependencies among the identified enablers were established. The robustness and practical applicability of the proposed model were further validated through expert assessment. In addition, Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was conducted to classify the enablers based on their driving power and dependence. The results revealed a six-level hierarchical structure, in which “government support and policy framework,” “top management commitment,” and “advanced knowledge and awareness of the circular economy” were identified as dominant driving enablers exerting significant influence over other factors. These findings provide a comprehensive and structured understanding of the systemic interactions among circular economy enablers and offer actionable insights for policymakers and industry practitioners in emerging economies. The study contributes to the existing body of knowledge by advancing a theoretically grounded and empirically validated framework that supports strategic prioritization and facilitates the transition toward a circular construction paradigm. ]]> Total Interpretive Structural Modeling of Circular Economy Enablers in the Construction Industry: Evidence from Bangladesh m. m. aflatun kawsar khondaker farhana shamim shohaib islam hasin md muhtasim taqi sudipa sarker syed mithun ali doi: 10.56578/jisda010104 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 2026 1 1 Article 58 10.56578/jisda010104 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010104 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010103 Choosing optimal materials for cryogenic storage systems is a challenging intelligent decision-making task with several competing requirements. To determine the best material for producing cryogenic tanks used in the transportation of liquid nitrogen, this study proposed an integrated multi-criteria decision-making (MCDM) framework that combined the Criteria Importance through Intercriteria Correlation (CRITIC) method with the Combinative Distance-based Assessment (CODAS) method. Seven technical performance criteria, including toughness index, yield strength, density, Young’s modulus, thermal expansion, thermal conductivity, and specific heat were adopted to assess seven potential materials. By considering both contrast intensity and intercriteria correlation, the CRITIC technique could scientifically establish criteria weights while reducing subjective bias. The options were ranked using the CODAS approach according to their Euclidean and Taxicab distances from the negative ideal solution. The findings demonstrated that density had the greatest management weight when it came to sustainable design. Therefore, aluminium 5052-O was the most appropriate material for cryogenic tank applications out of all the alternatives under investigation. The proposed CRITIC–CODAS framework, a dependable intelligent decision-support tool for strategic material selection in advanced manufacturing and engineering management contexts, exhibits robustness, transparency, and computing efficiency. 03-30-2026 <![CDATA[ Choosing optimal materials for cryogenic storage systems is a challenging intelligent decision-making task with several competing requirements. To determine the best material for producing cryogenic tanks used in the transportation of liquid nitrogen, this study proposed an integrated multi-criteria decision-making (MCDM) framework that combined the Criteria Importance through Intercriteria Correlation (CRITIC) method with the Combinative Distance-based Assessment (CODAS) method. Seven technical performance criteria, including toughness index, yield strength, density, Young’s modulus, thermal expansion, thermal conductivity, and specific heat were adopted to assess seven potential materials. By considering both contrast intensity and intercriteria correlation, the CRITIC technique could scientifically establish criteria weights while reducing subjective bias. The options were ranked using the CODAS approach according to their Euclidean and Taxicab distances from the negative ideal solution. The findings demonstrated that density had the greatest management weight when it came to sustainable design. Therefore, aluminium 5052-O was the most appropriate material for cryogenic tank applications out of all the alternatives under investigation. The proposed CRITIC–CODAS framework, a dependable intelligent decision-support tool for strategic material selection in advanced manufacturing and engineering management contexts, exhibits robustness, transparency, and computing efficiency. ]]> Optimum Material Selection for Cryogenic Tanks: An Integrated Criteria Importance Through Intercriteria Correlation–Combinative Distance-based Assessment Approach chiranjib bhowmik chinta haran majumder sumit das lala arpan kool payel deb pradeep kumar karsh krishanu chatterjee doi: 10.56578/jisda010103 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 2026 1 1 Article 44 10.56578/jisda010103 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010103 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010102 This study examines Smart Tourism Technologies (STTs) as a structured, decision-relevant system within sustainability-oriented contexts, where multiple interacting factors shape tourism outcomes under conditions of complexity and uncertainty. A PRISMA-guided systematic review of 78 peer-reviewed studies (2015–2025) is conducted to synthesise how STTs-related attributes, multi-level mechanisms, and contextual conditions influence travel experience outcomes. The analysis organises existing literature into a multi-level framework that connects core technological dimensions with mediating and moderating mechanisms and broader contextual enablers. Within this structure, these elements jointly determine how cognitive, affective, behavioural, and well-being outcomes emerge across different tourism settings. The evidence indicates that STTs operate through interdependent processes rather than isolated technological effects, involving factors such as security, personalisation, technology readiness, perceived value, and digital well-being. These factors can be understood as implicit decision variables shaping experience quality, satisfaction, and sustainable behavioural responses. The review also identifies a gradual shift in the literature from technology adoption perspectives toward more integrated analytical interpretations that combine experience evaluation, sustainability considerations, and decision-relevant reasoning. By reorganising fragmented findings into a coherent analytical structure, the study provides a basis for further modelling, comparative evaluation, and structured decision analysis in sustainability-oriented tourism systems. 03-30-2026 <![CDATA[ This study examines Smart Tourism Technologies (STTs) as a structured, decision-relevant system within sustainability-oriented contexts, where multiple interacting factors shape tourism outcomes under conditions of complexity and uncertainty. A PRISMA-guided systematic review of 78 peer-reviewed studies (2015–2025) is conducted to synthesise how STTs-related attributes, multi-level mechanisms, and contextual conditions influence travel experience outcomes. The analysis organises existing literature into a multi-level framework that connects core technological dimensions with mediating and moderating mechanisms and broader contextual enablers. Within this structure, these elements jointly determine how cognitive, affective, behavioural, and well-being outcomes emerge across different tourism settings. The evidence indicates that STTs operate through interdependent processes rather than isolated technological effects, involving factors such as security, personalisation, technology readiness, perceived value, and digital well-being. These factors can be understood as implicit decision variables shaping experience quality, satisfaction, and sustainable behavioural responses. The review also identifies a gradual shift in the literature from technology adoption perspectives toward more integrated analytical interpretations that combine experience evaluation, sustainability considerations, and decision-relevant reasoning. By reorganising fragmented findings into a coherent analytical structure, the study provides a basis for further modelling, comparative evaluation, and structured decision analysis in sustainability-oriented tourism systems. ]]> A Multi-Level Analytical Framework for Smart Tourism Technologies: A Systematic Review of Decision-Relevant Mechanisms and Sustainable Outcomes (2015–2025) mahjabeen ahmed razia sultana sumi imranul hoque doi: 10.56578/jisda010102 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 2026 1 1 Article 28 10.56578/jisda010102 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010102 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010101 Retailers frequently face stockouts and overstocking due to inaccurate demand forecasting, leading to financial losses and reduced customer satisfaction. This study proposes a data-driven framework to improve weekly sales forecasting at both aggregate and store levels using Walmart’s historical sales data. A hybrid methodology integrating time series models, regression techniques, deep learning, and a hierarchical structure is developed to capture temporal patterns and external demand factors. The proposed approach achieves high predictive accuracy, with a Mean Absolute Error (MAE) of 306,361.11, Root Mean Square Error (RMSE) of 528,096.34, and an R² of 0.99, outperforming traditional models. Beyond accuracy, the study emphasizes the role of forecasting as a decision-support tool. The results demonstrate that improved forecasts enable better operational decisions such as replenishment planning and safety stock optimization, while also supporting tactical and strategic decisions related to distribution, workforce planning, and supply chain design. Overall, the findings highlight that integrating hybrid forecasting models with decision-making processes can reduce inventory costs, enhance service levels, and support more efficient and sustainable retail operations. 03-30-2026 <![CDATA[ <p>Retailers frequently face stockouts and overstocking due to inaccurate demand forecasting, leading to financial losses and reduced customer satisfaction. This study proposes a data-driven framework to improve weekly sales forecasting at both aggregate and store levels using Walmart’s historical sales data. A hybrid methodology integrating time series models, regression techniques, deep learning, and a hierarchical structure is developed to capture temporal patterns and external demand factors. The proposed approach achieves high predictive accuracy, with a Mean Absolute Error (MAE) of 306,361.11, Root Mean Square Error (RMSE) of 528,096.34, and an <em>R</em>² of 0.99, outperforming traditional models. Beyond accuracy, the study emphasizes the role of forecasting as a decision-support tool. The results demonstrate that improved forecasts enable better operational decisions such as replenishment planning and safety stock optimization, while also supporting tactical and strategic decisions related to distribution, workforce planning, and supply chain design. Overall, the findings highlight that integrating hybrid forecasting models with decision-making processes can reduce inventory costs, enhance service levels, and support more efficient and sustainable retail operations.</p> ]]> Data-Driven Demand Forecasting for Retail Decision-Making: A Hybrid Machine Learning and Time Series Approach to Inventory Optimization nour ben said golam kabir ikbal chammakhi msadaa seyedmehdi mirmohammadsadeghi doi: 10.56578/jisda010101 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 Journal of Intelligent Sustainability and Decision Analytics 03-30-2026 2026 1 1 Article 1 10.56578/jisda010101 https://www.acadlore.com/article/JISDA/2026_1_1/jisda010101