Power battery third-party reverse logistics provider selection: Fuzzy evidential reasoning

Abstract

Massive power batteries (PBs) are crucial to new energy vehicle enterprises. Due to Extended Producer Responsibility (EPR), the third-party reverse logistics provider selection(3PRLPs) process has become an important decision to save cost. This paper uses an innovative combination of qualitative analysis and quantitative data integration to address the PB 3PRLPs problem by using Failure Modes and Effects Analysis (FMEA) and fuzzy evidential reasoning (FER). Firstly, the possible failures and potential effects in the PB 3PRLPs are identified by the FMEA to determine criteria and importance grades. Subsequently, AHP is utilized to calculate the criteria weight based on the importance of grades. FER is creatively applied to address the intersection of assessment grades and allocate the belief degree (BD) of the interaction to fuse heterogeneous data. Additionally, sensitivity analysis is done to look into the stability of the sequencing. Compared with other methods, the proposed method not only solves the subjectivity of AHP weighting but also manipulates probabilistic and fuzzy uncertainties for multi-criteria decision-making (MCDM). This method is useful in quantitatively analyzing the 3PRLPs problem and in providing auxiliary decision support for enterprises.