| Issue |
MATEC Web Conf.
Volume 100, 2017
13th Global Congress on Manufacturing and Management (GCMM 2016)
|
|
|---|---|---|
| Article Number | 02013 | |
| Number of page(s) | 11 | |
| Section | Part 2: Internet +, Big data and Flexible manufacturing | |
| DOI | https://doi.org/10.1051/matecconf/201710002013 | |
| Published online | 08 March 2017 | |
A Large Group Decision Making Approach Based on TOPSIS Framework with Unknown Weights Information
Department of Industrial Engineering, School of Mines; Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China; China University of Mining and Technology, Xuzhou, 221116, PR China
* Corresponding Email: xzhen_l@163.com
Large group decision making considering multiple attributes is imperative in many decision areas. The weights of the decision makers (DMs) is difficult to obtain for the large number of DMs. To cope with this issue, an integrated multiple-attributes large group decision making framework is proposed in this article. The fuzziness and hesitation of the linguistic decision variables are described by interval-valued intuitionistic fuzzy sets. The weights of the DMs are optimized by constructing a non-linear programming model, in which the original decision matrices are aggregated by using the interval-valued intuitionistic fuzzy weighted average operator. By solving the non-linear programming model with MATLAB®, the weights of the DMs and the fuzzy comprehensive decision matrix are determined. Then the weights of the criteria are calculated based on the information entropy theory. At last, the TOPSIS framework is employed to establish the decision process. The divergence between interval-valued intuitionistic fuzzy numbers is calculated by interval-valued intuitionistic fuzzy cross entropy. A real-world case study is constructed to elaborate the feasibility and effectiveness of the proposed methodology.
Key words: Multiple-attributes large group decision making / optimization of weights of DMs / non-linear programming model / TOPSIS
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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