Reducing inconsistency in pairwise comparisons using multi-objective evolutionary computing

Edward Abel, Ludmil Mikhailov, John Keane

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Pairwise comparisons are commonly used to estimate values of preference among a finite set of decision alternatives with regards to intangible factors. Inconsistency within decision making judgments may occur. This work proposes an approach to reducing inconsistency using multi-objective optimization with the objectives of different inconsistency types and judgment modification measures. The approach allows the decision maker to choose both the inconsistency measure(s) and the modification measure(s) employed to suit their needs and attitudes. Utilizing multi-objective optimization allows for a range of possible tradeoff solutions to be presented to the decision maker for selection, aiding them in their pursuit of inconsistency reduction. It also enables better understanding of the characteristics of the decision problem and its inconsistency.

Original languageEnglish
Title of host publication 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, Manchester
Place of PublicationPiscataway
PublisherIEEE
Pages80-85
Number of pages6
ISBN (Print)9780769551548
DOIs
Publication statusPublished - 2013
EventIEEE International Conference on Systems, Man, and Cybernetics - Manchester, United Kingdom
Duration: 13 Oct 201316 Oct 2013

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics
Country/TerritoryUnited Kingdom
CityManchester
Period13/10/1316/10/13

Keywords

  • Decision analysis
  • Evolutionary computing
  • Genetic algorithmns
  • Inconsistency
  • Multi-objective optimiszation

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