Analyzing a Closed-Loop Supply Chain Considering Environmental Pollution Using the NSGA-II

This paper investigates a closed-loop supply chain network that includes forward-reverse logistics and the uncertainty of demand in actual situations. The proposed mathematical model helps maximize total profit and minimize environmental pollution. The total costs considered include production costs, cargo transportation costs, and disposal costs. The model aims to answer two questions: 1) How many new and remanufactured products should be produced by manufacturers and transported in the supply chain?, and 2) How many recycled products must flow in the chain to maximize total profit and minimize environmental pollution? The nondominated sorting genetic algorithm II (NSGA-II) was used to analyze the approximated Pareto solutions of our proposed model. The experimental results show that the supply chain network can obtain higher profit and cause less pollution when the uncertainty of demand is considered. These results can provide effective suggestions for production decision making.