Abstract
Supply chain network design (SCND) is an important strategic decision determining the structure of each entity in the supply chain, which has an important impact on the long-term development of a company. An efficient and effective supply chain network is of vital importance for improving customer satisfaction, optimizing the allocation of resources, and increasing profitability. The environmental concerns and social responsibility awareness of the whole society have spurred researchers and managers to design sustainable supply chains (SSCs) integrating the economic, environmental, and social factors. In addition, the innate uncertainty of the SCND problem requires an integrated method to cope. In this regard, this study develops a multi-echelon multi-objective robust fuzzy closed-loop supply chain network (CLSCN) design model under uncertainty including all three dimensions of sustainability. This model considers the total cost minimization, carbon caps, and social impact maximization concurrently to realize supply chain sustainability, and is able to make a balance between the conflicting multiple objectives. Meanwhile, the uncertainty of the parameters is divided into two categories and addressed with two approaches: the first category is missed working days related to social impact, which is solved by the fuzzy membership theory; the second category is the demand and remanufacturing rate, which is settled by a robust optimization method. To validate the ability and applicability of the model and solution approach, a numerical example is conducted and solved using ILOG CPLEX. The result shows that the supply chain network structure and the value of the optimization objectives will change when considering sustainability and different degrees of uncertainty. This will enable supply chain managers to reduce the environmental impact and enhance the social benefits of their supply chain activities, and design a more stable supply chain to better cope with the influence of uncertainty.
Highlights
In the last few decades, with the growing emphasis on resource scarcity and environmental pollution, a closed-loop supply chain (CLSC) that integrates the traditional forward supply chain and reverse logistics has become an important research field in both academia and industry [1,2,3]
We developed a robust fuzzy mixed integer programming (MIP) optimization model for the closed-loop supply chain network (CLSCN) design problem considering sustainability and uncertainty
We aimed to minimize the costs of CLSCNs, as well as simultaneously minimizing missed working days by maximizing the fuzzy membership degrees
Summary
In the last few decades, with the growing emphasis on resource scarcity and environmental pollution, a closed-loop supply chain (CLSC) that integrates the traditional forward supply chain and reverse logistics has become an important research field in both academia and industry [1,2,3]. The summary from Casey et al [9] shows that most consumers have a strong sense of participation in waste recycling; they are willing to pay more to improve WEEE collection methods and to spend more on environmentally friendly products Another important reason for enterprises to adopt recycling activities is the economic potential of remanufactured products [10]. Other types of uncertain parameters in our model are the customer demand and the remanufacturing rate, which will obviously influence the tactical plan of the supply chain In this context, robust optimization is employed to ensure the stability of the network to some extent [29], and.
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