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Ranking
PHF-VIKOR - Probabilistic Hesitant extension of VIKOR
Probabilistic Hesitant outranking/ranking - Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)
Zhang, S., Xu, Z. S., He, Y.2017doi:10.1016/j.inffus.2017.02.001 ↗
Overview
phf-vikor extends VIKOR 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.
- Output
- utility, lower is better
- Data
- Hesitant, uncertainty tuples complete
- Weights
- Needs a weight source
- Size
- 2+ alternatives, 3-10 criteria works best
- Used for
- Probabilistic Hesitant MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms
Look elsewhere when
- •Crisp data sufficient - use base VIKOR directly (avoid unnecessary uncertainty layer)
- •Aggregation operator (PFWA/PFOWA/etc.) not specified - output ambiguous
Assumptions to verify
- Decision matrix entries are valid Probabilistic Hesitant numbers/tuples
- Underlying crisp method's compensation assumption holds in uncertain space
- All decision-maker(s) and experts use the same linguistic/uncertainty scale
Edge cases and pitfalls
Value-space violation: ensure all entries satisfy PHFE: {γ_k|p_k} where γ_k ∈ [0,1], Σ p_k ≤ 1 before computation.
Defuzzification method affects ranking: E[PHFE] = Σ γ_k p_k is the canonical choice but alternatives exist.
Works with
Commonly takes its weights from
How to cite
Zhang, S.; Xu, Z. S.; He, Y. (2017). Operations and integrations of probabilistic hesitant fuzzy information in decision making. Information Fusion. https://doi.org/10.1016/j.inffus.2017.02.001
System ID, as it appears in reports and the API
PHF-VIKOR