A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery

Abstract Product recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) closed-loop supply chain (CLSC) investigated to optimize facility location inventory control decisions by considering product recovery. The objective minimize the total cost operations system. We formulate as mixed-integer nonlinear programming model design modified hybrid differential evolution algorithm (MHDE) solve it efficiently. Finally, numerical results are presented validate performance new algorithm. show that MHDE more efficient effective than Lingo other algorithms for research under study. Managerial insights also derived managers improve their performance.