MCDMRankingcrisp
随机加性效用函数 (不确定性下的偏好分解)
STOCHASTIC-UTA (随机加性效用函数 (不确定性下的偏好分解)) 是一种排序多标准决策方法 (MCDM)，由 Stavrou, D. I.; Ventikos, N. P.; Tsoukalas, V. D. (2018) 引入 — STOCHASTIC-UTA 的开创性章节为 Jacquet-Lagrèze, E.; Siskos, J. (1982) — 经典 UTA 基础为 Siskos, Y. (1980) — 偏好分解理论于 1982 年提出 — 随机扩展为 Stavrou-Ventikos-Tsoukalas 2018 Springer。它将评估了多个标准的方案决策矩阵转化为结构化、可复现的结果。
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来源
- Stavrou, D. I., Ventikos, N. P., Tsoukalas, V. D. (2018). Robust Evaluation of Risks in Ship-to-Ship Transfer Operations: Application of the STOCHASTIC UTA Multicriteria Decision Support Method. In Lee, P. T. W. & Yang, Z. (Eds.), Multi-criteria Decision Making in Maritime Studies and Logistics (pp. 161–185). Springer. DOI: 10.1007/978-3-319-62338-2_8 ↗
如何引用本页
ScholarGate. (2026, June 2). Stochastic UTilités Additives (preference-disaggregation under uncertainty). ScholarGate. https://scholargate.app/zh/decision-making/stochastic-uta
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