The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers

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 the competitive logistics industry, a growing number of third-party logistics providers (3PLs) 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 repair facilities where returned products from retailers or end-customers were inspected, repaired, and refurbished for redistribution. To fill the void in such a line of research, this paper proposes a mixed-integer programming model and a genetic algorithm that can solve the reverse logistics problem involving the location and allocation of repair facilities for 3PLs. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example encountering 3PLs offering value-added services.