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

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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a method for resolving decision problems concerning multiple criteria using paraconsistent logic, Pavelka style fuzzy logic, and many-valued similarity. The method is demonstrated to be robust through the analysis of two data sets and comparisons with existing Multi-Attribute Decision Making approaches. The novel approach has an edge over existing methods in handling large size decision problems with numerous criteria and alternatives.