Hesitant Fuzzy MARCOS
HF-MARCOS (Hesitant Fuzzy MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, G., Geng, X., Yuan, Y. in 2020 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
HF-MARCOS extends crisp MARCOS (Stević 2020) to hesitant fuzzy data. Each cell is an HFE — a finite set of membership degrees in [0,1] representing multiple expert assessments or hesitation. Group decisions are aggregated cell-wise via HFWA (Xia-Xu 2011) before the MARCOS pipeline. HFEs are then collapsed to crisp scores via mean (default) prior to AI/AAI construction. The compromise utility f(K_i) balances proximity to ideal and distance from anti-ideal; higher is better.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- May exhibit rank reversal when alternatives are added to or removed from the set.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Li, G., Geng, X., Yuan, Y. (2023). An integrated MCDM method based on hesitant fuzzy MARCOS for supplier evaluation under sustainability requirements. Journal of Intelligent & Fuzzy Systems
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Cite this page
ScholarGate. (2026, June 2). HF-MARCOS. ScholarGate. https://scholargate.app/decision-making/hf-marcos