Reverse logistics network design for product reuse, remanufacturing, recycling and refurbishing under uncertainty

With the improvement of the quality of life in human society, the need to use more natural resources is felt more than ever. In this regard, much research has been done on restoring depreciated and consumed products to the supply chain; many factors, including the quality of returned products, can significantly impact how the reverse logistics network will be used. The two-stage stochastic mixed-integer programming model proposed in this paper considers various processes of recovering recyclable products, including reuse, refurbishing, remanufacturing, recycling, and selling spare parts. Also, considering uncertainty on quality and quantity of returned products, product variety, and bill of material are model features. Due to the computational complexity of largescale problems, such problems require considerable time to solve. To tackle this issue, a hybrid algorithm constructed by a genetic algorithm and branch and cut algorithm (with CPLEX solver) has been introduced, which can significantly reduce the solution time. Finally, the algorithm is applied to a real-world problem to design a reverse logistics network for a small-size laboratory equipment manufacturer.

Automated summary

With the improvement in human society's quality of life, there is a greater need for natural resources. Factors such as the quality of returned products significantly impact the reverse logistics network. A two-stage stochastic mixed-integer programming model and hybrid algorithm have been proposed to address large-scale problems, which were applied to a real-world scenario successfully.