Journal of applied mathematics & informatics

The Korean Society for Computational and Applied Mathematics (한국전산응용수학회)
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A DECISION-MAKER CONFIDENCE LEVEL BASED MULTI-CHOICE BEST-WORST METHOD: AN MCDM APPROACH

  • SEEMA BANO (Department of Mathematics & Statistics, Integral University) ;
  • MD. GULZARUL HASAN (Department of Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education) ;
  • ABDUL QUDDOOS (Department of Mathematics & Statistics, Integral University)
  • Received : 2022.12.09
  • Accepted : 2023.11.24
  • Published : 2024.03.30
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Abstract

In real life, a decision-maker can assign multiple values for pairwise comparison with a certain confidence level. Studies incorporating multi-choice parameters in multi-criteria decision-making methods are lacking in the literature. So, In this work, an extension of the Best-Worst Method (BWM) with multi-choice pairwise comparisons and multi-choice confidence parameters has been proposed. This work incorporates an extension to the original BWM with multi-choice uncertainty and confidence level. The BWM presumes the Decision-Maker to be fully confident about preference criteria vectors best to others & others to worst. In the proposed work, we consider uncertainty by giving decision-makers freedom to have multiple choices for preference comparison and having a corresponding confidence degree for each choice. This adds one more parameter corresponding to the degree of confidence of each choice to the already existing MCDM, i.e. multi-choice BWM and yields acceptable results similar to other studies. Also, the consistency ratio remained low within the acceptable range. Two real-life case studies are presented to validate our study on proposed models.

Keywords

Acknowledgement

The authors are thankful to the reviewers and editor of the journal for the improvement of the manuscript.

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