Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: A study of the Shanghai End-of-life vehicles industry

Abstract This study analyzes the effect of multi-attribute decision making (MADM) on the efficiency of the end-of-life vehicle (ELV) reverse logistics industry in the context of the circular economy to improve resource utilization efficiency. In this paper, the DEA-TOPSIS method, based on a prediction model of Triple Exponential Smoothing (TES), is adopted for multi-attribute decision making with a view to improving industry efficiency, Data Envelopment Analysis (DEA) is used to calculate the input and output indicators' efficiency values and the slack movements of the indicators of input and output decision-making unit's (DMU's) base with TES as the decision-making basis. Meanwhile, the Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is used to rank alternative decision-making schemes. Moreover, the ordering is also carried out using the Additive Weighting, Weighted Product and Elimination et Choice Translating Reality (ELECTRE) method. In this study, the DEA-TOPSIS method is used to make multi-attribute decisions about industry efficiency. Taking Shanghai's ELV industry as an example, this study utilizes 2017 data from seven member-enterprises of the Shanghai End-of-life Vehicle Professional Committee; it uses the DEA-TOPSIS method based on TES to conduct an empirical study on multi-attribute decision making to improve efficiency and analyze efficiency improvement through alternative decision-making schemes. The findings show that the DEA-TOPSIS method based on TES is effective for multi-attribute decision-making to improve the ELV reverse logistics industry's efficiency. The multi-attribute decision-making in this paper facilitates the management and investment decision making of the ELV recycling industry. It also provides an effective solution for managers and researchers in the ELV industry to improve its efficiency.

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