A paraconsistent many-valued similarity method for multi-attribute decision making

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this paper, we introduce a method for resolving decision problems concerning multiple criteria in relation to a finite set of decision alternatives. This approach makes use of paraconsistent logic, Pavelka style fuzzy logic and many-valued similarity. To demonstrate the robustness of the method, two data sets, one on the performance of five mobile phone operators in Ghana and the other, a numerical example have been analysed and the rankings compared correspondingly with those of three existing dominant Multi-Attribute Decision Making (MADM) approaches, namely Elimination and Choice Translating Reality II (ELECTRE II); Preference Ranking Organisation MeTHod for Enrichment Evaluation (PROMETHEE I and II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Apart from providing a ranking that is similar to these three famous outranking methods, the novel approach has the edge over them due to its ability to relatively handle large size decision problems - decision problems with numerous criteria and alternatives - without much difficulty.

Original languageEnglish
Pages (from-to)128-152
Number of pages25
JournalFuzzy Sets and Systems
Volume409
Early online date2020
DOIs
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed

Keywords

  • Decision-making
  • Fuzzy logic
  • Many-valued similarity
  • Multiple criteria evaluation
  • MV-algebras
  • Paraconsistent logic

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence

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