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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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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.

Abstract

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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.

Abstract

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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.

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