Σύγκριση μεθόδων

	PHFS-EHVaR	PHFS-HVaR
Πεδίο	Λήψη Αποφάσεων	Λήψη Αποφάσεων
Οικογένεια	MCDM	MCDM
Έτος προέλευσης	2017	2017
Δημιουργός	Zhou, W. Xu, Z.	Zhou, W. Xu, Z.
Τύπος	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.
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	—	—
Συναφείς	1	1
Σύνοψη	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.