Comparer des méthodes

	PHFS-EHVaR	PHFS-HVaR
Domaine	Prise de décision	Prise de décision
Famille	MCDM	MCDM
Année d'origine	2017	2017
Auteur d'origine	Zhou, W. Xu, Z.	Zhou, W. Xu, Z.
Type	Extended tail decision-making method for probabilistic hesitant fuzzy environments. EHVaR improves upon HVaR by computing the expected (weighted sum) value over the entire left tail, not just the boundary point. EHVaR(h, X) = Σ_{i=1}^{k-1} c_i·p_i + c_k·(X - Σ_{i=1}^{k-1} p_i) where k satisfies P_{k-1} < X ≤ P_k. Always strictly separates PHFEs that HVaR cannot distinguish. Supports group decision-making via dynamic weight programming model.	Tail decision-making method for probabilistic hesitant fuzzy environments. Input is a PHFE (probabilistic hesitant fuzzy element) — an HFE where each membership value c_l carries an explicit occurrence probability p_l with Σp_l=1. HVaR(h, X) is the boundary membership value at cumulative probability X: the largest c_k such that P(c ≤ c_k) ≥ X. Directly analogous to classical Value-at-Risk (VaR). Intended for risk-averse investors who focus on worst-case outcomes under a given certainty degree.
Source fondatrice	Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing	Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing
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Apparentées	1	1
Résumé	PHFS-EHVAR (PHFS-EHVaR — Expected Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	PHFS-HVAR (PHFS-HVaR — Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.