Abstract
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.
Highlights
Operational management of many areas are consisted by series of decision making issues
Inspired by the principle of consensus-based methods, a non-linear programming model optimizing the weights of the DMs through an iterative procedures among individual decision matrices and comprehensive decision matrix is proposed in this article
For the multiple attributes large group decision making (MALGDM) problem, the determination of the weights of criteria and the selection of decision framework should be seriously considered. As it can utilize the original decision information of DMs to reduce the subjectivity of some other weight methods such as analytic hierarchy process (AHP) [14,15] and Delphi [16], the information entropy theory is employed to calculate the weights of the criteria
Summary
Operational management of many areas are consisted by series of decision making issues. Based on the claimed that, many DMs in large group decision making had similar opinion, Tapia-Rosero et al [6] proposed a shape-similarity-based clustering approach to improve the weighting process of DMs in MALGDM. Comparing with the clustering-based methods, the consensus reaching process-based methods can incorporate the opinions of all DMs. Inspired by the principle of consensus-based methods, a non-linear programming model optimizing the weights of the DMs through an iterative procedures among individual decision matrices and comprehensive decision matrix is proposed in this article. For the MALGDM problem, the determination of the weights of criteria and the selection of decision framework should be seriously considered As it can utilize the original decision information of DMs to reduce the subjectivity of some other weight methods such as AHP [14,15] and Delphi [16], the information entropy theory is employed to calculate the weights of the criteria.
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