A Multi-Criteria Group Decision-Making Approach Based on Improved BWM and MULTIMOORA with Normal Wiggly Hesitant Fuzzy Information

Multi-criteria group decision-making (MCGDM) problems are widespread in real life. However, most existing methods, such as hesitant fuzzy set (HFS), hesitant fuzzy linguistic term set (HFLTS) and inter-valued hesitant fuzzy set (IVHFS) only consider the original evaluation data provided by experts but fail to dig the concealed valuable information. The normal wiggly hesitant fuzzy set (NWHFS) is a useful technique to depict experts’ complex evaluation information toward MCGDM issues. In this paper, on the basis of the score function of NWHFS, we propose the linear best-worst method (BWM)-based weight-determining models with normal wiggly hesitant fuzzy (NWHF) information to compute the optimal weights of experts and criteria. In addition, we present some novel distance measures between NWHFSs and discuss their properties. After fusing the individual evaluation matrices, the NWHF-ranking position method is put forward to develop the group MULTIMOORA method, which can be determined by the final decision results. Moreover, we investigate the Spring Festival travel rush phenomenon deeply and apply our methodology to solve the train selection problem during the Spring Festival period. Finally, the applicability and superiority of the proposed approach is demonstrated by comparing with traditional methods based on two aggregation operators of NWHFSs.