A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing

In this paper, a likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information is developed for the selection and evaluation of contractors in logistics outsourcing. First, various definitions and operations related to hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic sets (HFLSs) are discussed. Next, the definition of multi-hesitant linguistic term sets (MHFLTSs) is reviewed, which can eliminate the limitations associated with HFLTSs and HFLSs as well as emphasize the significance of repeated values. Then, a likelihood function is developed for multi-hesitant fuzzy linguistic term elements (MHFLTEs) based on a generalized function of the possibility degree of real numbers. Using this generalized function based on linguistic scale functions, alternatives that satisfy certain properties can be selected according to various semantic situations and the preferences of decision makers. Finally, the likelihood function of MHFLTEs is embedded into TODIM to address decision-making problems in which decision makers exhibit bounded rationality, and hesitance and repetitiveness exist in the linguistic evaluation information. According to the results of the illustrative examples and comparative analysis, the proposed approach can be used to effectively solve multi-criteria decision making problems involving the selection and evaluation of third-party logistics service providers.