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| PHFS-EHVaR× | PHFS-HVaR× | |
|---|---|---|
| Lĩnh vực | Ra quyết định | Ra quyết định |
| Họ | MCDM | MCDM |
| Năm ra đời | 2017 | 2017 |
| Người khởi xướng | Zhou, W. Xu, Z. | Zhou, W. Xu, Z. |
| Loại≠ | 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. |
| Công trình gốc | Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing DOI ↗ | Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing DOI ↗ |
| Tên gọi khác | — | — |
| Liên quan | 1 | 1 |
| Tóm tắt≠ | 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. |
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