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A Stability-Driven Hybrid MCDM Framework Based on Taxonomies of Decision-Making Processs Elements – an Empirical Case Study |
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PP: 1443-1490 |
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doi:10.18576/amis/200604
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Author(s) |
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Amany A. Slamaa,
Hussein M. Elshafie,
Ahmed I. Sharaf,
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Abstract |
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| Quantifying decision-making approaches like Hybrid Multi Criteria Decision-making (MCDM) addresses the vagueness associated with the decision-making process by leveraging the integration of at least two MCDM methods. Unfortunately, there is no guidance mechanism for combining MCDM methods for real decision-making problems, where MCDM methods are the core of Hybrid-MCDM, as well as MCDMs or Hybrid-MCDM, not addressed from a practical perspective. The study aims to recommend a stability-driven Hybrid-MCDM framework that aids in designing a decision-model based on the taxonomy of decision-making process elements. The proposed methodology integrates PRISMA with Design Science Research and Extended Taxonomy Design Process to introduce a guidance stability-driven framework for MCDM as well as Hybrid-MCDM that aids in designing a decision-model based on the taxonomy of decision-making process elements. Practically, it helps decision makers to design robustness decision-models. As implementation, the proposed framework recommends Neutrosophic ELECTRE-VIKOR to prioritize beneficiaries for donation management systems that have 21 constraints and 16 alternatives. Average Spearman rank correlation and deviation across five scenarios are 0.998 and 0.03 respectively. Further, Decision Stability Intervals and Monte Carlo simulations are introduced to test robustness. The comparative benchmark for MCDM methods (TOPSIS, ANP, and VIKOR) and the proposed Hybrid-MCDM model are introduced as evidence of conflict-resilient hybrid decision models. This prioritization issue faces decision makers in various domains such as healthcare, education, civil development, project management, the software industry, etc. The stability of this decision model is proved by sensitivity tests. The proposed model contributes to ranking priorities in these critical domains. |
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