MATEC Web Conf.
Volume 100, 201713th Global Congress on Manufacturing and Management (GCMM 2016)
|Number of page(s)||11|
|Section||Part 2: Internet +, Big data and Flexible manufacturing|
|Published online||08 March 2017|
- J. Qin, X. Liu, W. Pedrycz. A multiple attribute interval type-2 fuzzy group decision making and its application to supplier selection with extended LINMAP method. Soft Computing. 1–20 (2016). [Google Scholar]
- D. Guha, D. Chakraborty. Fuzzy multi-attribute group decision making method to achieve consensus under the consideration of degrees of confidence of experts’ opinions ☆. Computers & Industrial Engineering. 4, 493–504 (2011). [CrossRef] [Google Scholar]
- J. Xiong, Y.W. Chen, K.W. Yang. A decision support model for multi-attribute group decision making using a multi-objective optimization approach. International Journal of Computational Intelligence Systems. 2, 337–353 (2013). [CrossRef] [Google Scholar]
- S. Zahir. Clusters in a group: Decision making in the vector space formulation of the analytic hierarchy process. European Journal of Operational Research. 3, 620–634 (1999). [Google Scholar]
- N. Bolloju. Aggregation of analytic hierarchy process models based on similarities in decision makers’ preferences ☆. European Journal of Operational Research. 3, 499–508 (2001). [CrossRef] [Google Scholar]
- A. Tapia-Rosero, Bronselaer A, Tré G D. A method based on shape-similarity for detecting similar opinions in group decision-making. Information Sciences. 258, 291–311 (2014). [CrossRef] [Google Scholar]
- B. Liu, Y Shen, X Chen. A partial binary tree DEA-DA cyclic classification model for decision makers in complex multi-attribute large-group interval-valued intuitionistic fuzzy decision-making problems. Information Fusion. 1, 119–130 (2014). [CrossRef] [Google Scholar]
- B. Liu, Y. Shen, Y. Chen. A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment. Information Fusion. 23, 156–165 (2015). [CrossRef] [Google Scholar]
- I. Palomares, L. Martinez, F. Herrera. A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making. IEEE Transactions on Fuzzy Systems. 3, 516–530 (2014). [Google Scholar]
- X.H. Xu, X.Y. Zhong, X.H. Chen. A dynamical consensus method based on exit–delegation mechanism for large group emergency decision making. Knowledge-Based Systems. 86, 237–249 (2015). [CrossRef] [Google Scholar]
- F. Zhang, J. Ignatius, Y. Zhao. An improved consensus-based group decision making model with heterogeneous information. Applied Soft Computing. 35, 850–863 (2015). [CrossRef] [Google Scholar]
- K. Atanassov, G. Gargov. Interval valued intuitionistic fuzzy sets. Fuzzy Sets & Systems. 3, 343–349 (1989). [CrossRef] [Google Scholar]
- S.P. Wan, G.L. Xu, F. Wang. A new method for Atanassov’s interval-valued intuitionistic fuzzy MAGDM with incomplete attribute weight information. Information Sciences. 316, 329–347 (2015). [CrossRef] [Google Scholar]
- R. Al-Aomar. A combined AHP-entropy method for deriving subjective and objective criteria weights. International Journal of Industrial Engineering Theory Applications & Practice. 1, 12–24 (2010). [Google Scholar]
- Y. Yu, Y. Wu, N. Yu. Fuzzy comprehensive approach based on AHP and entropy combination weight for pipeline leak detection system performance evaluation// Systems Conference (SysCon), 2012 IEEE International. 1–6 (2012). [Google Scholar]
- I.A. Curtis. Valuing ecosystem goods and services: a new approach using a surrogate market and the combination of a multiple criteria analysis and a Delphi panel to assign weights to the attributes. Ecological Economics. 3–4, 163–194 (2004). [CrossRef] [Google Scholar]
- Y. Li, X. Chu, D. Chu. An integrated approach to evaluate module partition schemes of complex products and systems based on interval-valued intuitionistic fuzzy sets. International Journal of Computer Integrated Manufacturing 7, 675–689 (2013). [Google Scholar]
- K. Xu, J. Zhou, R. Gu. Approach for aggregating interval-valued intuitionistic fuzzy information and its application to reservoir operation. Expert Systems with Applications. 7, 9032–9035 (2011). [CrossRef] [Google Scholar]
- H. Zhang, L. Yu. MADM method based on cross-entropy and extended TOPSIS with interval-valued intuitionistic fuzzy sets. Knowledge-Based Systems. 2, 115–120 (2012). [CrossRef] [Google Scholar]
- K.T. Atanassov. Interval Valued Intuitionistic Fuzzy Sets. Intuitionistic Fuzzy Sets. Physica-Verlag HD. 343–349 1999. [Google Scholar]
- L.C. Jang, W.J. Kim, T. Kim. A Note on Distances between Interval-Valued Intuitionistic Fuzzy Sets. International Journal of Fuzzy Logic & Intelligent Systems. 1, 8–11 (2011). [CrossRef] [Google Scholar]
- S. Wang. A Novel Multi-attribute Allocation Method Based on Entropy Principle. Journal of Software Engineering. 1, 16–20 (2012). [Google Scholar]
- R. Simanaviciene, L. Ustinovichius. Sensitivity Analysis for Multiple Criteria Decision Making Methods: TOPSIS and SAW. Procedia - Social and Behavioral Sciences. 6, 7743–7744 (2010). [CrossRef] [Google Scholar]
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