Abstract
As cost pressures continue to mount in this era of economic slowdowns, a growing number of firms have begun to explore the possibility of managing product returns in a more cost-efficient and timely manner. However, few studies have addressed the problem of determining the number and location of initial collection points in a multiple time horizon, while determining the desirable holding time for consolidation of returned products into a large shipment. To fill the void in such a line of research, this paper proposes a mixed-integer, nonlinear programming model and a genetic algorithm that can solve the reverse logistics problem involving both spatial and temporal consolidation of returned products. The robustness of the proposed model and algorithm was tested by its application to an illustrative example dealing with products returned from online and retail sales.
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