Abstract
Abstract Inspired by the nonlinear weighted average operator, this paper proposes several generalized power average operators to aggregate hesitant fuzzy linguistic decision information. It is worth noting that the new operators take both the location and date weight information and the relative closeness of the decision-making information into consideration, a characteristic that results in objectivity and fairness in a group decision making. Moreover, we demonstrate some useful properties of the operators and discuss their associations. A new approach based on the designed operators is then proposed for hesitant fuzzy linguistic multiple attribute group decision-making problems, in which the attribute weights are known or unknown. Finally, this paper demonstrates the efficiency and feasibility of the proposed method through a numerical example.
Highlights
Multiple attribute group decision making (MAGDM), is at the core of decision making science and has been widely applied in many areas, including economic benefit evaluation [40], information technology improvement project selection [39], supply chain strategy selection [3], and tailing impoundment site selection [5]
Inspired by the nonlinear weighted average operator, this paper proposes several generalized power average operators to aggregate hesitant fuzzy linguistic decision information
By analyzing the existing hesitant fuzzy linguistic decision-making research, we found that the following problems exist: (1) Most existing ordered hesitant fuzzy linguistic operators are based on the partial orders of hesitant fuzzy linguistic term sets (HFLTSs)
Summary
Multiple attribute group decision making (MAGDM), is at the core of decision making science and has been widely applied in many areas, including economic benefit evaluation [40], information technology improvement project selection [39], supply chain strategy selection [3], and tailing impoundment site selection [5]. It is difficult for the decision maker to deeply participate in the decision-making process Based on these reasons, in this study, we introduce some new generalized weighted power-averaging aggregation operators to deal with hesitant fuzzy linguistic information and propose a new approach to solve MAGDM problems. (2) The ordered hesitant fuzzy linguistic operators defined in this paper are based on the total orders of HFLTSs. Our operators have the following attractive advantages: (1) When using the proposed operators to aggregate input arguments, HFLTS does not need to be converted into other fuzzy sets, reducing information loss, and, thereby, alleviating the influence of excessively large (or small) input arguments on the aggregation results.
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