Journal of Quality Engineering and Management

Abstract Purpose: This study aims to develop a durable closed-loop supply chain network capable of simultaneously addressing sustainability, resilience, agility, and digitalization while incorporating fuzzy–stochastic uncertainties. The significance of this research lies in the limitations of traditional supply chains, which often fail to perform effectively under severe environmental fluctuations, operational disruptions, and demand variability, thereby highlighting the need for intelligent and multidimensional decision-making frameworks.
Methodology: To achieve the research objectives, a structured three-phase framework was designed. In the first phase, demand subject to considerable uncertainty was forecast using the SARIMA time-series model to capture market volatility and seasonal patterns. In the second phase, supplier evaluation criteria were identified through a systematic literature review and expert judgment, and subsequently weighted via the Stochastic–Fuzzy Best–Worst Method (SFBWM). Supplier ranking was then performed using the Stochastic–Fuzzy TOPSIS (SFTOPSIS) technique. In the final phase, a multi-objective fuzzy–stochastic mathematical model was developed to design and optimize the supply chain network, while fuzzy–stochastic robust optimization was employed to address data uncertainty. The multi-objective problem was solved using a modified version of the Lexicographic–Chebyshev Multi-Choice Goal Programming Method (LCRMCGP).
Findings: A case study conducted in “Ebtakar Tajhiz Teb Yekta,” a company operating in the medical equipment industry, demonstrated that the proposed model effectively supports key strategic decisions, including the selection of primary and backup suppliers, the optimal location of collection and recycling centers, excess capacity allocation, and the choice of information-exchange technologies (traditional systems vs. blockchain-based platforms). The integration of IoT and blockchain technologies increased product return rates, reduced recycling costs, and enhanced transparency and sustainability across the network. Overall, the results confirm that the proposed framework can successfully balance economic, environmental, and social objectives while improving flexibility and resilience under uncertainty.
Originality/Value: The novelty of the present study lies in developing an integrated framework for designing a viable closed-loop supply chain under hybrid fuzzy–stochastic uncertainty. Unlike previous studies that mainly focused on isolated dimensions of supply chain management, this research simultaneously incorporates sustainability, resilience, agility, and digitalization within a multi-objective optimization model. Furthermore, the integration of SARIMA, SFBWM, SFTOPSIS, and LCRMCGP methods provides a more accurate and comprehensive decision-making process. Comparative results also demonstrate that the proposed model outperforms conventional approaches in reducing deviations, improving decision consistency, and enhancing overall network sustainability.

Abstract Purpose: This work addresses the automotive industry with two important, evolving concepts: sustainability and resiliency. The proposed model is designed to balance economic, environmental, and social objectives while maintaining adaptability to potential supply chain changes and disruptions. A real automotive company is then investigated as the case study to assess the applicability, validity, and performance of the developed model and, eventually, render useful managerial and decision aids.
Methodology: To achieve this objective, a comprehensive decision-making model has been developed. In the first stage, supplier evaluation is conducted based on sustainability and resilience criteria. This assessment employs two innovative decision-making approaches: the stochastic fuzzy Best-Worst Method (BWM) and stochastic VIKOR. In the subsequent stage, a multi-objective mathematical model is formulated by incorporating stochastic-fuzzy uncertainty. To solve the model, a fuzzy robust optimization approach combined with a modified multi-choice goal programming method based on a utility function is applied.
Findings: In this study, the supply chain of SAIPA Kashan Automotive Company is analyzed across three key dimensions: general criteria, sustainability, and resilience. Indicators such as cost, quality, and reductions in energy consumption were identified as the most critical evaluation factors. Supplier evaluation and ranking were carried out using the fuzzy VIKOR method. The results indicate that, among the main suppliers, the second and fifth options, and among the backup suppliers, the second option, received the highest scores. Furthermore, to assess the robustness and validity of the proposed approach, the results were compared with those obtained using conventional methods.
Originality/Value: The value of this research lies in presenting a comprehensive decision-making model under uncertainty and in improving supply chain performance across economic, environmental, and social perspectives. The findings can significantly assist managers and policymakers in the automotive industry in addressing complex supply chain challenges.

Abstract Purpose: The purpose of this paper is to address one of the most critical challenges faced by organizations today: controlling carbon dioxide emissions. This study aims to provide a model for designing a green supply chain network that minimizes total network costs while incorporating environmental considerations. The research seeks to achieve a balanced optimization of costs, carbon emissions, and service levels in supply chain management.
Methodology: This study proposes a novel integrated optimization model that considers economic, environmental, and customer satisfaction aspects within the supply chain network. The mathematical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. An exact method is employed to solve the model, which is coded and implemented using GAMS optimization software. The efficiency and effectiveness of the model are validated through numerical examples and data analysis.
Findings: The results demonstrate the model's ability to optimize both economic and environmental dimensions while maintaining high service levels and customer satisfaction. The numerical examples, solved for problems of varying dimensions, confirm the practicality and effectiveness of the proposed approach. The findings highlight the trade-offs between cost minimization, carbon emission reduction, and service quality in supply chain networks.
Originality/Value: This research contributes to the field by presenting a new integrated optimization model that simultaneously addresses cost efficiency, environmental sustainability, and customer satisfaction in green supply chain design. The use of a mixed-integer nonlinear programming approach and its implementation in GAMS provides a robust framework for solving complex supply chain problems. The study offers valuable insights for organizations aiming to achieve sustainability goals while maintaining economic viability and customer-centric operations.