Abstract:
In recent time, the economic and environmental benefits offered by the reverse supply chain process has become a strategic advantage to supply chain managers and practitioners. Consequently, many existing supply chains are seeking ways to add reverse activities by transforming into a closed-loop supply chain (CLSC) system. However, the sudden occurrence of a production disruption can make the overall process vulnerable if there is no suitable disruption recovery mechanism in place. The extant literature shows that little attention is paid to develop a disruption recovery model for a CLSC system considering both economic and environmental objectives. Thus, this study develops a non-linear complex mathematical model to minimize total costs, energy consumptions, CO2 emission, and waste generations of a supply chain system with a focus on disruption risks. This study also contributes to the literature by not only addressing the model with three existing heuristics named Multi-objective Genetic Algorithm (MOGA), Non-dominated sorting Genetic Algorithm (NSGA-II), Multi-objective Bonobo Optimizer (MOBO) but also by developing an updated hyper-heuristic algorithm based on choice function. Four performance metrices namely Algorithm Effort (AE), Ratio of Non-dominated Individual (RNI), Maximum Spread (MS), and Average Distance (AD) have been utilized to compare the efficiency and effectiveness of these algorithms. The numerical findings indicate that the integration of the reverse supply chain (RSC) can reduce more than 40% of the lost sales quantities to meet the production shortage during a production disruption in the supply chain. The RSC is also found to be successful by converting approximately 70% of the total collected waste into reusable goods. The suggested model is expected to assist industry managers to reduce the total supply chain costs after transforming the traditional supply chain into a CLSC system and also minimize the consumption of natural resources while following the environmental regulations.
