EconPapers    
Economics at your fingertips  
 

Ranking-Theory Methods for Solving Multicriteria Decision-Making Problems

Joseph Gogodze ()

Advances in Operations Research, 2019, vol. 2019, 1-7

Abstract: The Pareto optimality is a widely used concept for the multicriteria decision-making problems. However, this concept has a significant drawback—the set of Pareto optimal alternatives usually is large. Correspondingly, the problem of choosing a specific Pareto optimal alternative for the decision implementation is arising. This study proposes a new approach to select an “appropriate” alternative from the set of Pareto optimal alternatives. The proposed approach is based on ranking-theory methods used for ranking participants in sports tournaments. In the framework of the proposed approach, we build a special score matrix for a given multicriteria problem, which allows the use of the mentioned ranking methods and to choose the corresponding best-ranked alternative from the Pareto set as a solution of the problem. The proposed approach is particularly useful when no decision-making authority is available, or when the relative importance of various criteria has not been evaluated previously. The proposed approach is tested on an example of a materials-selection problem for a sailboat mast.

Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://downloads.hindawi.com/journals/AOR/2019/3217949.pdf (application/pdf)
http://downloads.hindawi.com/journals/AOR/2019/3217949.xml (text/xml)

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:hin:jnlaor:3217949

DOI: 10.1155/2019/3217949

Access Statistics for this article

More articles in Advances in Operations Research from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().

 
Page updated 2019-12-30
Handle: RePEc:hin:jnlaor:3217949