4.7 Article

Multiple attribute group decision making method based on interval-valued intuitionistic fuzzy power Heronian aggregation operators

期刊

COMPUTERS & INDUSTRIAL ENGINEERING
卷 108, 期 -, 页码 199-212

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2017.04.033

关键词

Multiple attribute group decision making; Interval-valued intuitionistic fuzzy set; Power average operator; Heronian mean; Interval-valued intuitionistic fuzzy power weighted Heronian aggregation (IVIFPWHA) operator

资金

  1. National Natural Science Foundation of China [71471172, 71271124]
  2. Special Funds of Taishan Scholars Project of Shandong Province [ts201511045]
  3. Shandong Provincial Social Science Planning Project [15BGLJ06, 16CGLJ31, 16CKJJ27]
  4. Teaching Reform Research Project of Undergraduate Colleges and Universities in Shandong Province [2015Z057]
  5. Key research and development program of Shandong Province [2016GNC110016]

向作者/读者索取更多资源

The power average operator can relieve the some influences of unreasonable data given by biased decision makers, and Heronian mean operator can consider the interrelationship of the aggregated arguments. In order to take full advantages of these two kinds of operators, in this paper, we combined the power average operator with Heronian mean operator and extended them to process interval-valued intuitionistic fuzzy information, and proposed the interval-valued intuitionistic fuzzy power Heronian aggregation (IVIFPHA) operator, interval-valued intuitionistic fuzzy power weight Heronian aggregation (IVIFPWHA) operator. At the same time, we presented a new similarity function of IVIFNs as support degree in power weighting, and it has a good reliability and accuracy. Further, some properties of these new aggregation operators are investigated and some special cases are discussed, and a new technique based on these operators for fuzzy multiple attribute group decision making (MAGDM) was presented. Finally, an illustrative example was given to illustrate the effectiveness and advantages of the developed method by comparing with the existing methods. (C) 2017 Elsevier Ltd. All rights reserved.

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