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Journal of Intelligent Sustainability and Decision Analytics
JISC
Journal of Intelligent Sustainability and Decision Analytics (JISDA)
JORIT
ISSN (print): 3134-6375
ISSN (online): 3134-6383
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2026: Vol. 1
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Journal of Intelligent Sustainability and Decision Analytics (JISDA) is a peer-reviewed, open-access journal dedicated to analytical and model-based approaches to sustainability-oriented decision-making in complex systems, projects, and policy environments. The journal addresses sustainability challenges as structured decision problems characterised by multiple objectives, competing criteria, and uncertainty. It publishes research that develops, evaluates, and applies formal decision models and quantitative frameworks to support robust and transparent sustainability decisions. JISDA welcomes methodological and applied contributions in areas such as multi-criteria decision analysis, structured evaluation frameworks, optimisation-informed assessment, uncertainty modelling, robustness analysis, and data-informed analytical methods, provided that a clearly defined decision or evaluation structure is central to the study. Interdisciplinary research drawing on decision science, operations research, systems modelling, environmental and energy studies, and sustainability analysis is encouraged, where analytical rigour and methodological transparency remain primary. The journal is published quarterly by Acadlore, with issues released in March, June, September, and December.

  • Professional Editorial Standards - All submissions are evaluated through a standard peer-review process involving independent reviewers and editorial assessment before acceptance.

  • Efficient Publication - The journal follows a defined review, revision, and production workflow to support regular and predictable publication of accepted manuscripts.

  • Open Access - JISDA is an open-access journal. All published articles are made available online without subscription or access fees.

Editor(s)-in-chief（1）
golam kabir
Faculty of Engineering and Applied Sciences, University of Regina, Canada; Department of Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals, Saudi Arabia
golam.kabir@uregina.ca | website
Research interests: Sustainable, Reliable, and Resilient Critical Infrastructure Systems; Circular Economy and Net-Zero Strategies; Sustainable and Resilient Supply Chain Management; Data Driven Decision-Making

Aims & Scope

Aims

Journal of Intelligent Sustainability and Decision Analytics (JISDA) is an international, peer-reviewed, open-access journal publishing research on analytical and decision-oriented approaches that support sustainability-related decision-making in complex systems, projects, and policy contexts. The journal addresses sustainability challenges as structured decision problems characterised by multiple objectives, competing criteria, uncertainty, and incomplete information. It examines how such problems are analysed and resolved through formal decision models, quantitative frameworks, and systematic evaluation methods.

The journal emphasises methodological contributions that advance the design, validation, and critical examination of decision analytics and modelling frameworks in sustainability contexts. While sustainability assessment constitutes an important component of this domain, JISDA extends beyond assessment exercises to include decision support, optimisation-informed evaluation, structured comparison of alternatives, and analytical reasoning that clarifies how sustainability choices are constructed and justified.

Submissions are expected to demonstrate explicit modelling structures, transparent assumptions, and rigorous validation procedures. Descriptive or purely thematic discussions without a clearly defined decision or evaluation framework fall outside the scope of the journal. JISDA operates at the intersection of sustainability studies, decision science, systems analysis, optimisation, and quantitative modelling, maintaining a strong commitment to analytical rigour, transparency, and reproducibility.

The journal publishes conceptual, methodological, computational, and applied research that strengthens robust and accountable sustainability-related decision-making across engineering, management, environmental, infrastructure, energy, and policy domains. JISDA is published quarterly by Acadlore and adheres to established international standards of peer review and editorial governance.

Key features of JISDA include:

  • Sustainability-related decision-making and evaluation are treated as analytical and methodological problems rather than as purely thematic discussions.

  • Emphasis is placed on formal decision analytics, modelling structures, and evaluation logic supporting structured comparison, prioritisation, optimisation, and choice under uncertainty.

  • Particular attention is given to the construction and validation of evaluation frameworks, indicator systems, weighting mechanisms, and decision rules, as well as to their influence on analytical outcomes.

  • The journal encourages examination of robustness, structural sensitivity, transparency, and empirical validation in sustainability decision-support models.

  • Ethical, societal, and governance dimensions are considered where they are formally embedded within decision structures, criteria systems, or modelling assumptions.

  • Comparative and cross-method studies are welcomed where they clarify how alternative analytical approaches yield differing conclusions across systems or sectors.

  • Editorial evaluation prioritises methodological clarity, reproducibility, and transparent modelling assumptions.

Scope

JISDA welcomes original research articles, theoretical contributions, systematic reviews, and analytically grounded empirical or computational studies that advance structured understanding of sustainability-oriented decision-making. Submissions should maintain a clear focus on decision analytics, modelling frameworks, or formal evaluation methods applied to sustainability challenges. Topics include, but are not limited to:

Sustainability-Oriented Decision and Evaluation Frameworks

  • Design and structuring of criteria systems, indicator hierarchies, and evaluation logic

  • Comparative evaluation of sustainability alternatives across systems and spatial scales

  • Integrated environmental, economic, and social decision models

  • Representation of uncertainty, ambiguity, and subjective judgement in formal sustainability analysis

  • Development of structured evaluation architectures for long-term sustainability planning

Decision Analytics and Multi-Criteria Methods

  • Multi-criteria decision analysis (AHP, ANP, TOPSIS, VIKOR, PROMETHEE, ELECTRE and related approaches)

  • Hybrid decision-support frameworks integrating quantitative and expert-based inputs

  • Sensitivity, stability, and robustness analysis of decision outcomes

  • Group decision-making and stakeholder preference modelling

  • Optimisation-informed evaluation and structured trade-off analysis

Uncertainty and Advanced Modelling Approaches

  • Probabilistic, fuzzy, evidential, Bayesian, interval, and robust modelling techniques

  • Formal treatment of incomplete or imprecise information in sustainability evaluation

  • Data-informed analytical approaches supporting structured decision processes

  • Integration of decision models with simulation, optimisation, and scenario analysis

  • Model validation, interpretability, and reproducibility

Applications in Sustainability-Oriented Systems

  • Decision analytics for energy systems, renewable deployment, decarbonisation strategies, and transition planning

  • Evaluation of circular economy strategies, resource efficiency policies, and waste management systems

  • Sustainability decision models for industrial, urban, transportation, and infrastructure systems

  • Comparative analysis of land-use planning and infrastructure development options

  • Decision support for long-term system transformation and climate resilience strategies

  • ESG-integrated analytical models in supply chains and infrastructure systems

Evaluation and Methodological Reflection

  • Benchmarking and comparative analysis of sustainability decision methodologies

  • Validation and performance assessment of decision-support frameworks

  • Critical examination of modelling assumptions and analytical bias

  • Robustness, transparency, and reproducibility in sustainability decision analytics

  • Methodological implications for policy design and strategic planning

Related and Emerging Topics

  • Dynamic and longitudinal sustainability evaluation

  • Scenario-based and exploratory decision analytics

  • Integration of evaluation models with optimisation, simulation, and system dynamics

  • Structured analysis of trade-offs and synergies across sustainability dimensions

  • Scaling decision models across local, regional, national, and global contexts

Articles
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Abstract

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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.

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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.

Abstract

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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.

Abstract

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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.

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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.

Abstract

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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.
Open Access
Research article
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
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Available online: 03-31-2026

Abstract

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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.
Open Access
Research article
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
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Available online: 03-31-2026

Abstract

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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.
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