Over the last decade, increased online purchases, stricter environmental standards, higher quality standards, and more lenient return policies have dramatically increased the volume of returned products and the complexity of the return process. To make matter more complicated, product returns are often volatile and uncertain in quantity, quality, and timing. The uncertain nature of product returns poses great challenges for collecting, sorting, testing, shipping, and disposing them. This paper proposes a nonlinear mixed-integer programming model and a genetic algorithm that can solve the stochastic network design problem in a closed-loop supply chain.
展开▼
