A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns

Traditionally, product returns have been viewed as an unavoidable cost of doing business, forfeiting any chance of cost savings. As cost pressures continue to mount in this era of economic downturns, a growing number of firms have begun to explore the possibility of managing product returns in a more cost-efficient manner. However, few studies have addressed the problem of determining the number and location 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. To fill the void in such a line of research, this paper proposes a nonlinear mixed-integer programming model and a genetic algorithm that can solve the reverse logistics problem involving product returns. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example dealing with products returned from online sales.