期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 22, 期 2, 页码 300-312出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2013.2253107
关键词
Atanassov's intuitionistic fuzzy set (A-IF); fuzzy set; mathematical programming; multiattribute group decision making (MAGDM); uncertainty
资金
- Key Program of the National Natural Science Foundation of China [71231003]
- National Natural Science Foundation of China [71061006, 61263018, 71171055, 71001015]
- Program for New Century Excellent Talents in University (the Ministry of Education of China) [NCET-10-0020]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20113514110009]
- Science and Technology Innovation Team Cultivation Plan of Colleges and Universities in Fujian Province
- Humanities Social Science Programming Project of Ministry of Education of China [09YGC630107]
- Natural Science Foundation of Jiangxi Province of China [20114BAB201012]
- Science and Technology Project of Jiangxi Province Educational Department of China [GJJ12265]
- Excellent Young Academic Talent Support Program of Jiangxi University of Finance and Economics
The aim of this paper is to develop a new Atanassov's intuitionistic fuzzy (A-IF) programming method to solve heterogeneous multiattribute group decision-making problems with A-IF truth degrees in which there are several types of attribute values such as A-IF sets (A-IFSs), trapezoidal fuzzy numbers, intervals, and real numbers. In this method, preference relations in comparisons of alternatives with hesitancy degrees are expressed by A-IFSs. Hereby, A-IF group consistency and inconsistency indices are defined on the basis of preference relations between alternatives. To estimate the fuzzy ideal solution (IS) and weights, a new A-IF programming model is constructed on the concept that the A-IF group inconsistency index should be minimized and must be not larger than the A-IF group consistency index by some fixed A-IFS. An effective method is developed to solve the new derived model. The distances of the alternatives to the fuzzy IS are calculated to determine their ranking order. Moreover, some generalizations or specializations of the derived model are discussed. Applicability of the proposed methodology is illustrated with a real supplier selection example.
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