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- 저자 : Seema Bano (검색결과 2 건)
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Seema Bano,Md Gulzarul Hasan,Abdul Quddoos 대한산업공학회 2023 Industrial Engineeering & Management Systems Vol.22 No.3
The best-worst method (BWM) is an advantageous mathematical method for solving the problem of prioritization in real-life decision-making problems. It helps to provide consensus decision-making by minimizing inconsistency and weighing the factors. BWM takes pairwise comparisons as input parameters. To address such issues where multiple options are assigned by experts to pairwise comparisons, Multi-choice BWM was developed. This method has shown its application in handling various choices and choosing that choice for which inconsistency is minimized. In this work, we have incorporated neutrosophic fuzziness in multiple options of pairwise comparisons in the form of trian-gular neutrosophic numbers. Neutrosophic numbers provide us with membership, non-membership, and indetermina-cy grades, which incorporate more information to handle uncertainty in real-life decision problems. We have pro-posed a mathematical framework to accommodate neutrosophic fuzzy theory, multiple choices, and best-worst method approaches for solving multi-criteria decision-making problems. To show the applicability of the proposed model and validate our study, the method has been experimented with three case studies. The results obtained are compared with previously proposed models. It shows that similar ranking orders are obtained using the proposed ap-proach.
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A Decision-Maker Confidence Level based Multi-Choice Best-Worst Method: An MCDM approach
Seema Bano,Md Gulzarul hasan,Abdul Quddoos 한국전산응용수학회 2024 Journal of applied mathematics & informatics Vol.42 No.2
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.
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