Consistent fuzzy preference relation with geometric Bonferroni mean: a fused preference method for assessing the quality of life
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Fuzzy preference relation (FPR) is commonly used in solving multi-criteria decision making problems because of its efficiency in representing people’s perceptions. However, the FPR suffers from an intrinsic limitation of consistency in decision making. In this regard, many researchers proposed the consistent fuzzy preference relation (CFPR) as a decision-making approach. Nevertheless, most CFPR methods involve a traditional aggregation process which does not identify the interrelationship between the criteria of decision problems. In addition, the information provided by individual experts is indeed related to that provided by other experts. Therefore, the interrelationship of information on criteria should be dealt with. Based on this motivation, we propose a modified approach of CFPR with Geometric Bonferroni Mean (GBM) operator. The proposed method introduces the GBM as an operator to aggregate information. The proposed method is applied to a case study of assessing the quality of life among the population in Setiu Wetlands. It is shown that the best option derived by the proposed method is consistent with that obtained from the other methods, despite the difference in aggregation operators.
KeywordsDecision making Fuzzy preference relation Geometric Bonferroni mean Decision matrix Quality of life
This study was supported by Niche Research Grant Scheme, Ministry of Higher Education, Malaysia and Universiti Malaysia Terengganu with vote no. NRGS 53131/7, the National Natural Science Foundation of China (Nos. 71501135 and 71771156), the 2019 Sichuan Planning Project of Social Science (No. SC18A007), the 2018 Key Project of the Key Research Institute of Humanities and Social Sciences in Sichuan Province (No. Xq18A01, No. LYC18-02), the Electronic Commerce and Modern Logistics Research Center Program, Key Research Base of Humanities and Social Science, Sichuan Provincial Education Department (No. DSWL18-2), the Spark Project of Innovation at Sichuan University (No. 2018hhs-43), and National Spanish project TIN2016-75850-R.
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