m eskandarpour - School of Industrial Engineering, Ecole des Mines de Nantes, France
a hasani - School of Industrial Engineering and Management, University of Shahrood, Shahrood, Iran

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 importantactivities of these systems is to assign returned products to suitable reverse manufacturing alternatives.In this study, a new approach based on the Evidential Reasoning Approach (ERA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed to deal with alternative(recovery system) selectionvia considering a comprehensive model in reverse logistics. This studycontributes to the literature with not only a novel reverse logistics decision modeling framework, but also a pragmatic data transformation technique which can comfort the combination of quantitative data and qualitative opinions using the evidential reasoning approach and TOPSIS. Finally, a case studyinthe automotive industry is used to demonstrate the efficiency of the proposed method in selectingsuitable reverse manufacturing alternatives. Th e company has to deal with the return products and make appropriate decision with respect to various criteria such cost, quality, and available resource.Uncertainty of returned products in terms of quantity, quality, and time complicates the decisionmaking process. The obtained results indicate a good compliance with experts’ opinions and efficiency of the proposed hybrid decision making method (i.e., ER-TOPSIS) to offer a complete ranking

Product Recovery , Reverse Logistic , TOPSIS , ERA , Incomplete Assessment , Group-AHP