Information aggregation operators based on hesitant fuzzy sets and prioritization relationship

Abstract

Hesitant fuzzy set (HFS) is a very useful technology in dealing with the situation that decision makers are hesitant among several values when asked to give evaluation information for alternatives. The aim of this paper is to aggregate the hesitant fuzzy information with prioritization relationship. Using the operations on HFSs, we first give a method to model the linear prioritization relationship among the aggregated hesitant fuzzy elements (HFEs) and determine the weighting vector of these elements by the operations of HFEs. Then some novel hesitant fuzzy prioritized aggregation operators are defined based on the ordered weighted average operator, the generalized weighted average operator, the quasi weighted average operator and the ordered modular average operator. Based on the proposed operators, we develop a hesitant fuzzy multi-attribute decision-making (MADM) method. Finally, a real decision problem is provided to illustrate the rationality and effectiveness of proposed operators. Compared with the existing hesitant fuzzy prioritized aggregation operator, the proposed process deriving the weights of the aggregated elements can avoid information loss as far as possible, and the proposed aggregation ways can consider both the aggregation requirements of decision makers and capture the prioritization phenomenon among the aggregated hesitant fuzzy elements.