Research Article

Multi-Criteria Decision-Making Methodology for Ranking and Selecting Optimal Metaverse Platform

Authors

Shimaa S. Mohamed * Department of Computer and Informatics, Zagazig University, Zagazig, Egypt.
Ahmed M. Ali Department of Computer and Informatics, Zagazig University, Zagazig, Egypt.

https://doi.org/10.22105/scfa.v1i1.18

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Abstract

This study adopted a decision-making model for ranking and section best Metaverse platforms. This section has various criteria such as effectiveness, safety, user experience, etc. So, the Multi-Criteria Decision-Making (MCDM) methodology is used to deal with multiple criteria. The Multi-Attributive Border Approximation area Comparison (MABAC) method is an MCDM methodology used to rank the alternatives. The proposed methodology is applied to nine criteria and fifteen alternatives. The criteria weights are computed. The sensitivity analysis is conducted to show the stability of the final rank. The ten cases in criteria weights are proposed, and ten ranks are shown. The results show the rank of alternatives under different cases is stable. The comparative analysis is conducted with other MCDM methods such as TOPSIS, VIKOR, EDAS, and COPRAS. The results show the proposed methodology is effective with other MCDM methods.

Keywords

Multi-criteria decision making Metaverse platform Selection problem MABAC method

References (18)

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Published 2024-01-25
Issue Vol. 1 No. 1 (2024): Soft Computing Fusion with Applications
Section Articles
Document Type Research Article
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How to Cite

Mohamed, S. S., & Ali, A. M. (2024). Multi-Criteria Decision-Making Methodology for Ranking and Selecting Optimal Metaverse Platform. Soft Computing Fusion With Applications , 1(1), 10-18. https://doi.org/10.22105/scfa.v1i1.18

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