A Reverse Logistics Network Design Method Using Genetic Algorithm

Abstract

Driven by ever growing concern to environments, legislative regulation and economic profitability, more and more firms pay attentions to physical design of reverse logistics networks. This paper considers the problem of determining the numbers and locations of centralized return centers (i.e., reverse consolidation points) where returned products from retailers or end-customers were collected, sorted and consolidated into a large shipment destined for manufacturers' or distributors' repair facilities. A stochastic nonlinear mixed integer programming model for the reverse logistics problem involving product returns is established. Genetic algorithm and Monte Carlo Method are used to solve the proposed model. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example dealing with products returned from online sales.