An Improved Differential Evolution Algorithm for a Multicommodity Location‐Inventory Problem with False Failure Returns

Customer returns are a common phenomenon in many industries, and they have a significant impact on business organizations and their supply chains. False failure returns are returned products that have no functional or cosmetic defects, and they represent a large body of customer returns in practice. In this paper, we develop a mixed‐integer nonlinear programming model to study a multicommodity location‐inventory problem in a forward‐reverse logistics network. This model minimizes the total cost in this network by considering false failure returns, and it also considers many real‐world business scenarios in forward and reverse logistics flows. Moreover, we design a new heuristic approach to solve the model efficiently. Finally, numerical experiments are conducted to validate our solution approach and provide meaningful managerial insights.