期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 93, 期 -, 页码 286-301出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2015.12.027
关键词
Multi-attribute group decision making; Triangular intuitionistic fuzzy number; Generalized ordered weighted averaging; Generalized hybrid weighted averaging; Multi-objective programming
资金
- National Natural Science Foundation of China [71061006, 61263018, 11461030]
- Natural Science Foundation of Jiangxi Province of China [20114BAB201012, 20142BAB201011]
- Twelve five Programming Project of Jiangxi Province Social Science [13GL17]
- Science and Technology Project of Jiangxi province educational department of China [GJJ15]
- Young scientists Training object of Jiangxi Province [20151442040081]
- Excellent Young Academic Talent Support Program of Jiangxi University of Finance and Economics
The aim of this paper is to develop some new generalized aggregation operators for triangular intuitionistic fuzzy numbers (TIFNs) and apply to multi-attribute group decision making (MAGDM) problems. First, the weighted possibility attitudinal expected values of TIFNs are defined and a new method is presented to rank TIFNs considering risk attitude of decision maker (DM). The sensitivity analyses on attitudinal character parameter are given. Then, the triangular intuitionistic fuzzy weighted averaging (TIFWA) operator, ordered weighted averaging (TIFOWA) operator, ordered weighted geometric (TIFOWG) operator and hybrid weighted averaging (TIFHWA) operator are defined. We further develop some new generalized aggregation operators for TIFNs, involving the triangular intuitionistic fuzzy generalized ordered weighted averaging (TIFGOWA) operator and generalized hybrid weighted averaging (TIFGHWA) operator. Some desirable properties for these operators are discussed in detail. Utilizing the TIFGHWA and TIFWA operators, we propose a new method for MAGDM with TIFNs and incomplete weight information. In this method, DMs' weights are determined by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the weights of attributes are objectively derived through constructing a multi-objective programming model which is transformed into a linear goal program to solve. Finally, the example analysis of an investment selection example verifies the effectiveness and practicability of the proposed method in this paper. (C) 2015 Elsevier Ltd. All rights reserved.
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