Designing a Reverse Logistics Network Considering Risk Management

According to the complexity and risk of the reverse logistics, the main objective of this paper is to propose a multistage methodology for designing a reverse logistics network from the risk management perspective. Firstly, we applied fuzzy DEA model to assess the candidate reverse logistics suppliers based on the risk management. Furthermore, we founded a mixed integer programming model by considering the risk measure of every reverse logistics supplier. Lastly, we designed a hybrid genetic algorithm to solve the mixed integer programming model to allocate the reverses logistics needs and select the optimal reverse logistics supplier combination. Moreover, we analyzed a numerical example to illustrate the feasibility of the methodology studied in this paper to consider the risk management. The research will improve the traditional location-allocation model to satisfy the risk environment, and develop the risk management theory in the reverse logistics fields.