Multi-objective closed-loop green supply chain model with disruption risk

The benefits of the circular economy are pushing industries towards forming closed-loop supply chains (CLSCs). This transition requires the industries to deal with conventional cost minimization along with various environmental objectives. However, the objectives become difficult to attain if the production process gets disrupted and no suitable recovery mechanism is in place. The extant literature indicates that few researchers have worked to develop a recovery model for CLSC systems that considers both economic and environmental objectives. Thus, this study develops a nonlinear complex mathematical model to minimize the total cost, energy consumption, CO₂ emission, and waste generation of supply chains with a focus on disruption risk. This research contributes to the literature by addressing the model with three existing heuristics — multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective bonobo optimizer (MOBO)–and by developing an updated hyper-heuristic algorithm based on a choice function. We employ four performance metrics–algorithm effort (AE), ratio of non-dominated individual (RNI), maximum spread (MS), and average distance (AD)–to compare the efficiency and effectiveness of these algorithms. Our quantitative results show that RSCs can mitigate production shortages stemming from supply chain disruptions. They also demonstrate the benefits of CLSCs with regard to lowering costs, energy consumption, CO₂ emissions, and waste generation.