期刊
KNOWLEDGE-BASED SYSTEMS
卷 106, 期 -, 页码 206-219出版社
ELSEVIER
DOI: 10.1016/j.knosys.2016.05.046
关键词
Multiple attribute decision making; Group decision making; Dynamic context; Individual attributes; Individual alternatives; Consensus
资金
- NSF of China [71571124, 71171160]
- Sichuan University [skqy201606]
- FEDER funds [TIN2013-40658-P]
- Andalusian Excellence Project [TIC-5991]
In classical multiple attribute group decision making (MAGDM), decision makers evaluate predefined alternatives based on predefined attributes. In other words, the set of alternatives and the set of attributes are fixed throughout the decision process. However, real-world MAGDM problems (e.g., the decision processes of the United Nations Security Council) frequently have the following features. (1) Decision makers have different interests, and they thus use individual sets of attributes to evaluate the individual alternatives. In some situations, the individual sets of attributes may be heterogeneous. (2) In the decision process, decision makers do not have to reach a consensus regarding the use of the set of attributes. Instead, decision makers hope to find an alternative that is approved by all or most of them. (3) Finally, both the individual sets of attributes and the individual sets of alternatives can change dynamically in the decision process. By incorporating the above practical features into MAGDM, this study defines a complex and dynamic MAGDM problem, and proposes its resolution framework. In the resolution framework, a selection process in the context of heterogeneous attributes is proposed that obtains the ranking of individual alternatives and a collective solution. In addition, a consensus process is developed that generates adjustment suggestions for individual sets of attributes, individual sets of alternatives and individual preferences, thus helping decision makers reach consensus. Compared with existing MAGDM models, this study provides a flexible framework to form an approximate decision model to real-world MAGDM problems. (C) 2016 Elsevier B.V. All rights reserved.
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