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ABSTRACT
With the increasing occurrence frequency of emergency events, how to select the most desirable alternative has emerged as one critical issue in emergency management. In this paper, we propose a new method combining entropy weight and DEMATEL (decision-making trial and evaluation laboratory) to manage emergency alternative selection under group decision makers. Firstly, IFN (intuitionistic fuzzy number) is introduced to represent linguistic assessment of decision makers under different criteria. Secondly, based on belief entropy, the weight of decision makers is determined and then applied in IFWA (intuitionistic fuzzy weighted averaging) operator to fuse group assessment. Thirdly, with the weight of criteria calculated by DEMATEL, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is used to rank emergency alternatives. Two case studies are illustrated to demonstrate the efficiency and practicability of the proposed method through the comparison of other existing methods. The proposed method has three advantages: (1) Via IFN and IFWA operator, it is simple and efficient to address the representation and fusion of linguistic assessment under uncertain information. (2) DEMATEL can help decision-makers weight the importance of criteria considering their direct and indirect effects; (3) Belief entropy can well measure the uncertainty of information and is capable of determining the weight of experts objectively.
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Luyuan Chen
Luyuan Chen received the B.S. degree in Computer Science from Southwest University, Chongqing, China in 2017. Currently, she is the graduate student in School of Computer Science, Northwestern Polytechnical University, Xian, China. Her research interests mainly focus on decision making, information fusion, three-way decisions and clustering.
Zhen Li
Zhen Li received the B.S. degree in Computer Science from Southwest University, Chongqing, China in 2017. Currently, she is pursuing the Ph.D. degree in Industrial Engineering and Management at Peking University. Her research interests are focused on data mining, prognosis and health management through advanced data analytics, quality and reliability engineering. She is a member of IEEE and INFORMS.
Xinyang Deng
Xinyang Deng received the bachelor's and master's degrees, and Ph.D. degree from Southwest University, Chongqing, China, in 2010, 2013, and 2016, respectively. Since 2017, he is an associate professor at the School of Electronics and Information, Northwestern Polytechnical University. His main research interests include multi-source information fusion, Dempster-Shafer evidence theory, uncertain information modelling and processing.