Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 30, Issue 1, Pages 121-132Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.3033062
Keywords
Atanassov-type intuitionistic fuzzy sets; majority voting rule (MVR); multicriteria decision-making (MCDM); ordered weighted average (OWA) aggregation; supplier selection
Funding
- National Natural Science Foundation of China [62003280, 61976120]
- Research Project of Education and Teaching Reform in Southwest University [2019JY053]
- Fundamental Research Funds for the Central Universities [XDJK2019C085]
- Chongqing Overseas Scholars Innovation Program [cx2018077]
- Natural Science Foundation of Jiangsu Province [BK20191445]
- Qing Lan Project of Jiangsu Province
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This article proposes a quantification method for Atanassov-type intuitionistic membership grades and uses majority voting rules to quantify membership degrees. The uncertainty of information and preferences of decision-makers are quantified, and properties and logical operations of measurement value are studied. The Takagi-Sugeno model is recommended for assigning values to tuning parameters K, and two models for multicriteria decision-making problem are presented. A numerical example of supplier selection demonstrates the competitive performance of the proposed method in terms of efficiency and feasibility.
Orderly Atanassov-type intuitionistic membership grades would be required in decision-making problems, however, sometime they are not completely ordered. To solve this problem, in this article we propose a quantification method for Atanassov-type intuitionistic membership grades, and use it to rank them. According to the majority voting rules, we introduce the measurement function for membership degree. We quantify the uncertainty of information and the preferences of decision-makers conveyed through intuitionistic fuzzy sets. We then use the introduced surrogates to construct the measurement for membership grades. The properties and some logical operations of measurement value are also studied. We recommend using the Takagi-Sugeno model and method to assign values to tuning parameters K. Moreover, we present two models for multicriteria decision-making problem, which use the measurement to determine the ranking between sets. Finally, a numerical example of supplier selection is given to show the competitive performance of the proposed method in terms of efficiency and feasibility.
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