Comprehensive Decision Modeling of Reverse Logistics System: A Multi-criteria Decision Making Model Using Hybrid Evidential Reasoning Approach and TOPSIS

In the last two decades, product recovery systems have received increasing attention due to several reasons such as new governmental regulations and economic advantages. One of the most important activities of these systems is to assign returned products to suitable reverse manufacturing alternatives. Uncertainty of returned products in terms of quantity, quality, and time complicates the decision making process. In this study, a new approach based on Evidential Reasoning Approach (ERA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed to deal with alternative (recovery system) selection via considering a comprehensive model in reverse logistics. Application of ERA helps us to take into account the experts’ quantitative and qualitative opinions in an uncertain environment due to various reasons such as incomplete assessment as well as imprecise and missing information simultaneously. Then, TOPSIS is used to rank alternatives that were evaluated by ERA. Finally, a case study in the automotive industry is used to demonstrate the efficiency of the proposed method in selecting suitable reverse manufacturing alternatives.