A new fuzzy multi-attribute group decision-making method with generalized maximal consistent block and its application in emergency management.

Decision-making is the most important business activity and becomes more complex in the current big-data situation. Most organizational decision-making is made in a group and has the data analytics function to seek specific answers for specific purposes. Multi-attribute group decision-making (MAGDM) methods provide effective support to decision groups by evaluating and integrating individual group members opining. However, current MAGDM methods often suffer from the problem of opining data and decision environment uncertainty, which is particularly severe in a large decision group or a newly decision problem. As a solution, rough sets and fuzzy sets have been applied in MAGDM to deal with data and decision process uncertainties. Although a lot of efforts have been made in applying rough sets and fuzzy sets to deal with data and decision process uncertainties the area, the disadvantage of rough set models in classification accuracy when similar class as basic knowledge granularity has not been well solved yet. This paper aims to solve this problem by introducing a new concept-maximal consistent block (MCB) and multi-granulation decision-theoretic rough set (MCB-MDTRS) models. Firstly, it establishes a binary tolerance relation on the universe of discourse, defines generalized MCBs, and introduces pessimistic DTRS based on MCBs. Secondly, it extends objective set to a fuzzy environment and proposes four MCB-MDTRFS models with consideration of the weight of each attribute. The steps of the proposed fuzzy MAGDM methods are carefully described in detail. Different from existing fuzzy MAGDM methods, the weight of each attribute is considered in the determination of the positive ideal decision objective and negative ideal decision objective in this paper, named by qualified and unqualified (fuzzy) sets. We solve the vague problem of individual preference evaluation and the uncertainty of setting ideal decision objectives by using the advantages of fuzzy set and rough set theory. Finally, it takes emergency plan selection as a case study to analyze the effectiveness of our methods and compare with other fuzzy MAGDM methods. Our methods outperform the selection of basic knowledge granularity, the determination of ideal decision objective sets and the ranking method, so as to increase the reliability and accuracy of ranking evaluation index. [ABSTRACT FROM AUTHOR]