A Novel Integrated Group Decision-Making Framework for Assessing Green Supply Chain Strategies Under Complex Uncertainty
| dc.contributor.author | Khan, Shah Zeb | |
| dc.contributor.author | Akhtar, Yasir | |
| dc.contributor.author | Salameh, Wael Mahmoud Mohammad | |
| dc.contributor.author | ETAL.. | |
| dc.date.accessioned | 2026-07-01T07:19:50Z | |
| dc.date.available | 2026-07-01T07:19:50Z | |
| dc.date.issued | 2026 | |
| dc.description | The growing global emphasis on environmental sustainability, climate change mitigation, and regulatory compliance has elevated the importance of green supply chain management (GSCM) as a strategic approach for sustainable industrial development [1]. GSCM integrates environmentally responsible practices across procurement, manufacturing, transportation, distribution, and end-of-life management to reduce carbon emissions, optimize resource utilization, and enhance operational efficiency [2]. Despite widespread adoption of strategies, such as green procurement, eco-friendly supplier selection, sustainable transportation, reverse logistics, and circular economy initiatives [3], evaluating and prioritizing these diverse strategies remains challenging due to differences in operational mechanisms [4], environmental impacts [5], economic implications [6], regulatory requirements [7], and implementation maturity [8]. | |
| dc.description.abstract | Green supply chain management (GSCM) has become essential for organizations seeking to balance environmental sustainability, regulatory compliance, and economic resilience. However, selecting appropriate green supply chain strategies constitutes a complex multicriteria decision-making (MCDM) problem due to diverse sustainability practices, conflicting objectives, dynamic market conditions, and significant uncertainty in expert evaluations. To address these challenges, this study proposes an intelligent multicriteria group decision-making (MCGDM) framework to assess 15 GSCM strategies across 15 environmental, operational, economic, and regulatory criteria. The framework employs complex fractional orthopair fuzzy sets (Formula presented.) to model uncertainty, expert hesitation, and complex-valued judgments. Expert weights are determined using the analytic hierarchy process (AHP), while criteria weights are derived objectively through the entropy method. A modified technique for order preference by similarity to the ideal solution (TOPSIS) is applied to obtain a robust ranking of alternatives. Evaluations from five multidisciplinary experts ensure practical relevance and validity. The results indicate enhanced uncertainty modeling, improved ranking stability, and greater interpretability compared with conventional fuzzy and deterministic approaches. The proposed framework provides a transparent and effective decision support tool for strategic GSCM planning. Keywords: entropy method, fractional orthopair fuzzy set; green supply chain management, sustainable decision-making, TOPSIS method. | |
| dc.identifier.citation | Khan, S. Z., Akhtar, Y., Salameh, W. M. M., Karabasevic, D., & Stanujkic, D. (2026). A Novel Integrated Group Decision-Making Framework for Assessing Green Supply Chain Strategies Under Complex Uncertainty. Systems, 14(4), 418. | |
| dc.identifier.doi | https://doi.org/10.3390/systems14040418 | |
| dc.identifier.uri | https://repository.adu.ac.ae/handle/1/8322 | |
| dc.language.iso | en | |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
| dc.title | A Novel Integrated Group Decision-Making Framework for Assessing Green Supply Chain Strategies Under Complex Uncertainty | |
| dc.type | Article |