期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 30, 期 6, 页码 1698-1711出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2021.3064695
关键词
Best-worst method (BWM); intuitionistic fuzzy preference relation; intuitionistic fuzzy set; linear programming model; multicriteria decision-making
资金
- National Natural Science Foundation of China [62141302, 11861034]
- Humanities Social Science Programming Project of Ministry of Education of China [20YJA630059]
- Natural Science Foundation of Jiangxi Province of China [20212BAB201011]
This article presents a new intuitionistic fuzzy BWM (IFBWM) method for multicriteria decision-making. The article formulates the derivation of optimal IF weights as an IF decision-making problem and constructs mathematical programming models. It also investigates the process of improving the consistency. Several examples are provided to demonstrate the effectiveness of the proposed IFBWM.
Best-worst method (BWM) has attracted increasing attention. It has been generalized to different fuzzy environments and applied to various real-life decision problems. This article develops a new intuitionistic fuzzy (IF) BWM (IFBWM) for multicriteria decision-making. When a decision maker (DM) makes comparisons, there may he some hesitancies. Thus, the reference comparisons are represented as intuitionistic fuzzy values (IFVs), the Best-to-Others vector and the Others-to-Worst vector are IF vectors. According to the multiplicative consistency of intuitionistic fuzzy preference relation, this article gives the consistency equations and views them as IF equations. The derivation of optimal IF weights of criteria is formulated as an IF decision-making problem. Thereby, a mathematical programming model is constructed to assure that the derived optimal IF weights of criteria is a normalized IF weight vector. Depending on the risk preference of DM, four linear programming models are presented to obtain the optimal IF weights based on the constructed mathematical programming model for the optimistic DM, the pessimistic DM, and the neutral DM, respectively. Furthermore, this article investigates the process of improving the consistency. Several examples are demonstrated to show the application and effectiveness of the proposed IFBWM.
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