A Bayesian Method for Decision of Weight for MADM Model with Interval Data

Author

Xuan, Sun ; Qinzhou, Niu ; Hefei, Xu

Author_Institution

Dept. of Electron. & Comput. Sci., Guilin Univ. of Technol., Guilin

fYear

2009

fDate

22-24 Jan. 2009

Firstpage

319

Lastpage

323

Abstract

The weight in TOPSIS approach (technique for order preference by similarity ideal solution - TOPSIS ) is given by experts or decision makers. The value of weight would be influenced by expertspsila subjective judgments. A slight difference in value of weight may result in diversity of order of alternatives. In this paper, a Bayesian method for decision of weight for MADM model with interval data is introduced. The value of weight is decided by prior information (other expertspsila knowledge, or numerical simulation etc.) and experts´ knowledge (or decision makerspsila experience/preference). This method effectively takes advantage of expertspsila knowledge and avoids the problem with expertspsila subjectivity. An illustrative example is showed to explore the applications of proposed method. The method is valuable for field of multi-attribute decision-making with interval data.

Keywords

Bayes methods; decision making; decision theory; Bayesian method; MADM model; TOPSIS approach; interval data; multiattribute decision-making; order preference technique; similarity ideal solution; Bayesian methods; Cities and towns; Computer science; Decision making; Information analysis; Information processing; Neural networks; Numerical simulation; Open wireless architecture; Sun; Bayesian networks; Decision of weights; Interval data; Multi-attribute decision-making (MADM); TOPSIS;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control, 2009. ICACC '09. International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-3330-8

Type

conf

DOI

10.1109/ICACC.2009.41

Filename

4777359