Designing Sustainable Closed-Loop Supply Chain Network Based on a Circular Economy Approach: Under Uncertainty During the Post-COVID Era

Sustainable closed-loop supply chain networks (SCLSCNs) are a key component of the circular economy and aim to reduce waste and increase resource efficiency. The multi-product SCLSCN under uncertainty during the post-COVID pandemic is presented in this study. After establishing a mathematical model for the design of a closed-loop network (CLN), a series of potential scenarios are used to account for parameter uncertainty. In this paper, a hybrid metaheuristic method is proposed to tackle this NP-hard problem for solving the multi-objective (MO) optimization. We refer to the hybrid algorithm as HWS (a hybrid method of WOA and SA). It is an optimizer that uses the relatively new whale optimization algorithm (WOA) and the widely used simulated annealing (SA) technique. Hybrid metaheuristics are one of the most interesting current trends in MO optimization. The aim of utilizing SA is to increase the yield by searching the most promising regions found by the WOA algorithm. Analytical and numerical data are given to illustrate the reliability and suitability of the proposed model using real case studies in the electronics industry. Finally, a sensitivity analysis is conducted to assess how consumer demand affects different aspects of sustainability. The results emphasize that the achievement of economic, environmental, and social goals is significantly affected by COVID-19. The scope of the economic aspect thus tends to increase with increasing changes in customer demand in the post-COVID-19. The bad social effects increased due to the number of days lost due to illness. These effects continue in the post-disaster period. On the other hand, carbon dioxide (CO2) emissions have decreased due to lockdowns during the disaster, leading to greater health and it continues after COVID-19.