Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model

Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today's conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions.