DocumentCode
2852442
Title
Simplification of decision making matrix in fuzzy multiple attribute decision making
Author
Pei, Zhi ; Zheng, Li
Author_Institution
Dept. of Ind. Eng., Zhejiang Univ. of Technol., Hangzhou, China
fYear
2011
fDate
6-9 Dec. 2011
Firstpage
36
Lastpage
40
Abstract
In the multiple attribute decision making, the computational intensity of problems quickly increases with the number of attributes increases. In this paper, we will introduce the simplification theory and technology of decision matrix in fuzzy multiple attribute decision making, and propose an attribute reduction algorithm. The purpose of this algorithm is to find all of the simplest order-preserving attribute subsets and the order-preserving attribute core of the corresponding multiple attribute decision making problem. One of the simplest order-preserving attribute subsets will be exploited as a substitute of the original attribute set. Thus the original multiple attribute decision making problem could be simplified and unnecessary cost related to redundant attributes could be saved. Finally, an illustrative numerical example is given to demonstrate our approach. And through this example, a dissimilarity between the attribute reduction in decision making and the knowledge reduction in rough set theory is discovered.
Keywords
decision making; fuzzy set theory; matrix algebra; rough set theory; attribute reduction algorithm; decision making matrix; fuzzy multiple attribute decision making; knowledge reduction; order-preserving attribute subsets; rough set theory; simplification theory; Decision making; Delta modulation; Engines; Fuzzy sets; Workstations; Attribute reduction; Decision making matrix; Fuzzy multiple attribute decision making; TOPSIS;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location
Singapore
ISSN
2157-3611
Print_ISBN
978-1-4577-0740-7
Electronic_ISBN
2157-3611
Type
conf
DOI
10.1109/IEEM.2011.6117874
Filename
6117874
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