Managing product returns in customer-centric e-tail environments

Owing to liberal customer service policies and the rapid product obsolescence resulting from ever-shortening product life cycles, product returns have become daily routines for many retailers. As product returns slow down the retailer's supply chain and consequently affect the retailer's bottom line, a growing number of retailers have begun to explore the possibility of managing product returns in a more cost-efficient and timely manner. Such a possibility includes the determination of the number and location of initial collection points and the establishment of the desirable holding time for consolidation of returned products into a large shipment. With this in mind, this paper proposes a mixed-integer programming model and a genetic algorithm that can solve the reverse logistics problem involving consolidation of returned products. The validity of the proposed model and algorithm was tested by their application to an illustrative example dealing with products returned from online retail sales.