A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling

Electric vehicle power battery recycling (EVPBR) is an effective way to utilize resources and reduce environmental damage. In order to ensure the safety and efficiency of electric vehicle batteries (EVBs) reuse and remanufacture, many EVB manufacturers are seeking cooperation with third-party reverse logistics (3PRL) providers to conduct the pre-treatment and transportation of post-used batteries. However, the selection of 3PRL provider is a matter of complex multi-criteria decision-making (MCDM) affected by numerous associated factors. The goal of this paper is to present a linguistic Pythagorean hesitant fuzzy MULTIMOORA method to investigate the selection of 3PRL providers for EVPBR. Firstly, given that the complexity of evaluation information, the linguistic Pythagorean hesitant fuzzy set (LPHFS) is defined in detail. On the basis of new evaluation representation tool, the weight determining models for expert panel and criteria set based on correlation consensus degrees and maximum deviation are derived, respectively. Then a MULTIMOORA method under the linguistic Pythagorean hesitant fuzzy environment is constructed. Later, the evaluation criteria system for the provider capability analysis is set up, and the proposed MCDM approach is applied to select the most suitable 3PRL provider for EVPBR in China. Finally, the sensitivity and comparative analysis results justify the robustness and feasibility of the proposed method.