System Engineering and Productivity

System Engineering and Productivity

Increasing the Accuracy of Calculations and Reducing Pairwise Comparisons Based on Clustering Decision Criteria and the Fuzzy Best-worst Method

Document Type : Research Paper

Author
Mohammad-Ali Eghbali
Assistant Professor, Department of Industrial Engineering, Faculty of Computer and Industrial Engineering, Birjand University of Technology, Birjand, Iran
10.22034/sep.2022.243409
Abstract
In some cases, due to the ambiguity caused by qualitative judgments, the multiplicity of decision criteria, and the resulting increase in calculations, researchers use different methods to eliminate a number of criteria based on the employer's opinions. However, in real conditions, eliminating some seemingly unimportant criteria may ultimately lead to poor results in selecting the best option. This article seeks to address these shortcomings and present a new combined approach to increase the accuracy of calculations and reduce pairwise comparisons to determine the optimal weights of decision criteria. Therefore, first, the decision criteria are scored based on the combination of the decision maker's opinions and the Shannon entropy method and clustered using the K-means method. Then, the weighting process of the criteria in each cluster is performed separately using the best-worst fuzzy method. In order to ensure the accuracy of the results, several numerical examples are also provided. The results show that the proposed hybrid approach, in addition to preventing the elimination of some decision criteria, leads to increased accuracy in calculations and reduced pairwise comparisons between criteria compared to the best-worst method. In other words, the results of this approach, while requiring less comparative data, also provide more reliable answers to the decision maker.
Keywords
Decision criteria clustering
Fuzzy best-worst method
Shannon entropy
Consistency rate
Multi-criteria decision making

Copyright ©, Mohammad-Ali Eghbali

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity
Volume 2, Issue 2 - Serial Number 3
Serial No. 3, Summer Quarterly
Summer 2022
Pages 25-48

  • Receive Date 07 July 2022
  • Revise Date 09 August 2022
  • Accept Date 13 August 2022
  • First Publish Date 13 August 2022
  • Publish Date 23 August 2022