A method based on the ideal and nadir solutions for stochastic MADM problems

Yan-Ping Jiang, Hai-Ming Liang and Minghe Sun ()
Additional contact information
Minghe Sun: UTSA

Working Papers from College of Business, University of Texas at San Antonio

Abstract: Many real life decision making problems can be modeled as stochastic multi-attribute decision making (MADM) problems. A novel method for stochastic MADM problems is developed based on the ideal and nadir solutions as in the classical TOPSIS method. In a stochastic MADM problem, the evaluations of the alternatives with respect to the different attributes are represented by discrete stochastic variables. According to stochastic dominance rules, the probability distributions of the ideal and nadir variates, both are discrete stochastic variables, are defined and determined for a set of stochastic variables. A metric is proposed to measure the distance between two discrete stochastic variables. The ideal solution is a vector of ideal variates and the nadir solution is vector of nadir variates for the multiple attributes. As in the classical TOPSIS method, the relative closeness of an alternative is determined by its distances from the ideal and nadir solutions. The rankings of the alternatives are determined using the relative closeness. Examples are presented to illustrate the effectiveness of the proposed method. Through the examples, several significant advantages of the proposed method over some existing methods are discussed.

Keywords: stochastic MADM; TOPSIS; ranking; ideal and nadir solutions; relative closeness (search for similar items in EconPapers)
JEL-codes: C61 C63 C69 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://interim.business.utsa.edu/wps/mss/0012MSS-061-2014.pdf Full text (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsa:wpaper:0178mss

Access Statistics for this paper

More papers in Working Papers from College of Business, University of Texas at San Antonio Contact information at EDIRC.
Bibliographic data for series maintained by Wendy Frost ().