Abstract
Today, the research on the closed-loop supply chain network design with sustainability and resiliency criteria is a very active research topic. This paper provides a new closed-loop supply chain under uncertainty with the use of resiliency, sustainability, and reliability dimensions among the first studies. To model this problem, a two-stage stochastic programming approach is used. To create robust solutions against uncertainty, a conditional value at risk criterion is contributed. The proposed model aims to minimize the total cost, environmental pollution, and energy consumption while maximizing the job opportunities as the social factor. In addition to the sustainability goals, the energy consumption is considered to be the last objective to be minimized. To show the applicability of the proposed model, an automobile assembler industry is applied. To solve the model, the Lp-metric method is employed to transform this multi-objective model into a single objective one. Since this closed-loop supply chain model is complex and NP-hard, a Lagrangian relaxation method with fix-and-optimize heuristic is employed to find the upper and lower bounds for the model via different random test problems. With an extensive analysis, the proposed model shows an improvement to the total cost, CO2 emissions, job opportunities and energy consumption.
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