Probabilistic Hesitant extension of TOPSIS
PHF-TOPSIS (Probabilistic Hesitant extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by PENDING_LITERATURE_SEARCH. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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When to use it
phf-topsis extends TOPSIS to handle Probabilistic Hesitant uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs) algebra. The final scores are defuzzified via E[PHFE] = Σ γ_k p_k before ranking.
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.
Sources
- PENDING_LITERATURE_SEARCH (). PENDING — PHF-TOPSIS specific seminal not confirmed. Zhang et al. 2017 (doi:10.1016/j.inffus.2017.02.001) is the foundational PHFS paper, not a PHF-TOPSIS paper. L.formulation.en cites 'Zhu & Xu 2018' as PHF-TOPSIS anchor — unverified. Candidate from search: Naeem et al. 2021 'Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information' (JIFS, doi:10.3233/JIFS-202700) — not confirmed as the canonical seminal.. link ↗
How to cite this page
ScholarGate. (2026, June 2). Probabilistic Hesitant extension of TOPSIS. ScholarGate. https://scholargate.app/en/decision-making/phf-topsis
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