A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability

A systematic review of modeling approaches in green supply chain optimization

Over the past decade, the significance of optimizing green supply chain management (GSCM) has gained unprecedented attention from both scholars and industry professionals. This surge in interest has led researchers to employ diverse modeling approaches in the pursuit of enhancing green supply chain networks. In this systematic review, we analyze 159 recent GSCM optimization papers published from 2017 to 2022 and identify the recent trends in mathematical modeling, multi-objective optimization, and the modeling/solver tools utilized. We find that the primary green focus is on minimizing carbon emissions (n = 44), reflecting the increasing concern for environmental sustainability. Among the modeling approaches employed, mixed-integer linear programming has emerged as the most popular choice (n = 51), followed by game theory-based modeling (n = 30). When it comes to multiobjective optimization, the ε-constraint approach is the most widely used. Evolutionary algorithms have emerged as the dominant meta-heuristic optimization approach. Additionally, the widely utilized solver in this domain is CPLEX with the most popular modeling/solver combination being GAMS/CPLEX. Moreover, the Journal of Cleaner Production was the leading outlet for research in this domain (n = 35). In addition to these findings, this study also discusses some other research trends and future research directions. Finally, we discuss the theoretical, managerial, and policy implications of this study. By providing GSCM researchers and practitioners with the latest trends in GSCM optimization approaches, this study contributes to the further advancement of the field.

A Fuzzy Robust Programming Model for Sustainable Closed-Loop Supply Chain Network Design with Efficiency-Oriented Multi-Objective Optimization

Sustainable closed-loop supply chain (SCLSC) network design and decision-making is a critical problem for enterprises and organizations’ operations because of its excellent economic, environmental, and social performance. This article proposes a multi-objective mixed-integer programming model with targets for minimum total cost, reduction in environmental damage, and maximum social responsibility. In order to deal with the uncertainty caused by the dynamic business environment, a fuzzy robust programming (FRP) approach is applied. Furthermore, an efficiency-oriented optimization methodology, hybridizing meta-heuristics and efficiency evaluation, is proposed to solve the developed multi-objective model and functions as auxiliary decision-making. Data envelopment analysis is applied to evaluate the sustainability performance of feasible solutions and calculate their efficiency. The efficiency can comprehensively reflect the sustainability performance and guide the evolution process of meta-heuristic algorithms. A numerical case validates the proposed FRP model and efficiency-oriented optimization methodology. The results demonstrate that with the proposed methodology, decision-makers not only can obtain a set of efficient schemes but also can determine the optimal scheme with the best sustainability performance.

A Review on Remanufacturing Reverse Logistics Network Design and Model Optimization

Remanufacturing has gained great recognition in recent years due to its economic and environmental benefits and effectiveness in the value retention of waste products. Many studies on reverse logistics have considered remanufacturing as a key node for network optimization, but few literature reviews have explicitly mentioned remanufacturing as a main feature in their analysis. The aim of this review is to bridge this gap. In total, 125 papers on remanufacturing reverse logistics network design have been reviewed and conclusions have been drawn from four aspects: (1) in terms of network structure, the functional nodes of new hybrid facilities and the network structure combined with the remanufacturing technologies of products are the key points in the research. (2) In the mathematical model, the multi-objective function considered from different aspects, the uncertainty of recovery time and recovery channel in addition to quantity and quality, and the selection of appropriate algorithms are worth studying. (3) While considering product types, the research of a reverse logistics network of some products is urgently needed but inadequate, such as medical and furniture products. (4) As for cutting-edge technologies, the application of new technologies, such as intelligent remanufacturing technology and big data, will have a huge impact on the remanufacturing of a reverse logistics network and needs to be considered in our research.

Using Fuzzy Sets and Markov Chain Method to Carry out Inventory Strategies with Different Recovery Levels

In this study, we first analyze the usability of recycling products, and use the fuzzy set method to determine the main impact on recycling items and their corresponding weights by using the Analytic Hierarchy Process (AHP) to identify various impact recycling levels. The Group Decision Supporting System (GDSS) determines the test standards for the recycling rating. It provides a convenient way for recyclers or manufacturers to classify their own products and use fuzzy numbers to select a set of test standards. It can deduce the recovery rate and remanufacturing rate of different recycling processing levels through the Markov chain model to find out the inventory model and total cost. In the numerical analysis, we found that a recycling rate of more than 90% is probably a necessary decision. Since the processing cost of the 100% recovery rate is doubled, the inventory level and total cost will increase with it. Therefore, this study was combined with the reverse logistics method to find the appropriate decision-making strategy and plan, such as the optimal inventory level and recovery rate.