An integrated approach to multiple criteria decision making based on the average solution and normalized weights of criteria deduced by the hesitant fuzzy best worst method

摘要

Best worst method (BWM) plays an important role in deducing the weights of criteria for multiple criteria decision making problems. In this paper, the BWM with hesitant fuzzy information is investigated and three different models to derive the priorities of criteria are developed with respect to diverse objectives. One is the score-based weight-determining model in which the score values of hesitant fuzzy elements (HFEs) are used to denote the most possible values of hesitant fuzzy elements while other possible values are ignored. The second model extends HFEs to the ones with equal lengths according to decision-makers' attitude, and then introduces a normalization technique to reduce the redundant weight solution space of hesitant fuzzy weights. The third model traverses each possible value of pairwise comparisons without ignoring or adding any information, and then calculates normalized weights with the highest reliability. Furthermore, by utilizing the normalized weights of criteria acquired by the hesitant fuzzy BWM, the evaluation based distance from average solution (EDAS) method is extended to the hesitant fuzzy environment and the procedure of the method is given for the convenience of application. A case study on choosing commercial endowment insurance products is solved by the proposed method. Comparative analyses with other existing methods are provided to show the validity and stability of the proposed method.