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: The goal of this research is to establish a design framework based on reliability for submarine pressure hulls, with the aim of attaining an ideal equilibrium between structural integrity and reliability.
Methodology: Initially, a mechanical Finite Element Model (FEM) was created and verified by comparing it to experimental data. Following that, various alternative models created through weight optimization algorithms were formulated. Uncertainties were represented using random variables, and reliability assessments were performed for each design.
Findings: The findings suggest that optimized models, even with reduced weights, can provide satisfactory failure probabilities. The prioritization derived from the reliability analysis offers a clear view of the final design.
Originality/Value: This study's uniqueness stems from its combined application of finite element analysis, uncertainty modeling, and optimization methods in reliability-based pressure hull design, a strategy seldom utilized in marine structural design.

Abstract Currently, rapid economic change and increasing competitive market pressure are pushing organizations to focus on making supply chain operations more efficient and effective. Proper design and efficiency of logistics networks as part of supply chain planning, in addition to creating a sustainable competitive advantage, increases customer satisfaction and provides the opportunity to meet their needs, which is why the decisions related to the design of these networks are of great importance. Enjoy. Therefore, in this study, the design of a closed-loop logistics network to reduce pollution and environmental pollution using the Bertsimas and wire stabilization method was presented. The mathematical model to be presented in this research was presented by considering the objectives of minimizing transportation costs, minimizing the time of receiving raw materials from the supplier and minimizing the time of product return from the customer to the separation center. Due to the strategic nature of the closed-loop supply chain, which with the approximate solution space causes a lot of costs to be delivered to the system to increase the accuracy of the answers of the mathematical model and application of this goal in this study It is used to reduce the computational time of the model, the results obtained with high accuracy. On the other hand, because the operational logic of solving Lagrange release is based on a single-objective model, first multi-objective mathematical model with Augmented Epsilon-Constraint The target was converted and then the Lagrange release algorithm was implemented on it.

Abstract The design of the closed loop supply chain network is one of the most important and fundamental strategic decisions, with the proper design of which creates a desirable structure and facilitates the efficient management of the chain effectively. One of the most fundamental problems in the design of the supply chain network is the closed loop of uncertainty in the quality of the return products, due to the emergence of this problem, the lack of accurate and accurate information as well as the dynamics and complexity of the chain components. This research in the design space the closed loop supply chain network is for durable products, which in the short term cannot be damaged and allow the reuse of parts in the production of new products, recycling or sales on the secondary market. The main purpose of this study is to use a two-stage randomized programming model and maximize expected earnings for all of the quality status scenarios in which the target function is a combination of revenue from the sale of products and recycled materials and components Recovered, in addition to fixed costs for centers, processes, logistics and transportation. Due to the complexity of the model, the problem was used with the Lp-shape and CPLEX algorithms and the GAMS software was used to solve the problem. Based on the results of the research, the substantive response introduced by CPLEX for the C3 to C6 test questions is significantly far from the optimal responses obtained by the L-Shape method. 

Abstract The issue of performance of complex engineering systems in environments with high uncertainty is one of the topics of research in the field of device engineering. Uncertainties and hazards in unfamiliar environments significantly affect the value provided by complex engineering systems to its stakeholders. Therefore, empowering complex systems to operate in conditions of uncertainty is inevitable. Obviously, before taking any action to improve the design of these devices to achieve greater capability, it is necessary to determine the index and model to measure their capability in the face of uncertainty. In this study, which is a combination of qualitative, quantitative, applied and fundamental research methods in terms of the type of research; After reviewing the research records, biodegradability has been introduced as an indicator to evaluate the capability of complex engineering systems for operations in conditions of uncertainty. Operating environment and three-level modeling of the operating environment, product functions and physical equipment of the product are its key features. Finally, the applicability of the proposed model is shown through a case study of an imaging satellite as a complex engineering system. The accuracy of the proposed model has been confirmed based on the analysis of the results of the case study and a questionnaire completed by experts.