Minimising a spectral risk objective, defined as a convex combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail estimation error. We propose Adaptive Conditional Forest Sampling (ACFS), a four-phase simulation-optimisation framework that integrates Generalised Random Forests for decision-conditional distribution approximation, CEM-guided global exploration, rank-weighted focused augmentation, and surrogate-to-oracle two-stage reranking before multi-start gradient-based refinement. We evaluate ACFS on two structurally distinct data-generating processes: a decision-dependent Student-t copula and a Gaussian copula with log-normal marginals, across three penalty-weight configurations and 100 replications per setting. ACFS achieves the lowest median oracle spectral risk on the second benchmark in every configuration, with median gaps over GP-BO ranging from 6.0% to 20.0%. On the first benchmark, ACFS and GP-BO are statistically indistinguishable in median objective, but ACFS reduces cross-replication dispersion by approximately 1.8 to 1.9 times on the first benchmark and 1.7 to 2.0 times on the second, indicating materially improved run-to-run reliability. ACFS also outperforms CEM-SO, SGD-CVaR, and KDE-SO in nearly all settings, while ablation and sensitivity analyses support the contribution and robustness of the proposed design.

翻译：针对谱风险目标（定义为期望成本与条件风险价值CVaR的凸组合）的最小化问题，当不确定性分布具有决策依赖性时，代理建模与基于仿真的排序均易受尾部估计误差影响。本文提出自适应条件森林采样（Adaptive Conditional Forest Sampling，ACFS），一种四阶段仿真优化框架，融合广义随机森林实现决策条件分布逼近、CEM引导的全局探索、排序加权聚焦增强以及代理模型到真实模型的二阶段重排序，最后执行多起点梯度求精。我们在两种结构截然不同的数据生成过程上评估ACFS：决策依赖的Student-t copula和高斯copula（含对数正态边缘分布），涵盖三种罚权重配置，每配置进行100次重复实验。在第二个基准测试的所有配置中，ACFS实现最低中位真实谱风险，相较于GP-BO的中位差距为6.0%至20.0%。在第一个基准测试中，ACFS与GP-BO的中位目标值在统计上无显著差异，但ACFS将跨重复实验的离散程度降低约1.8至1.9倍（第一个基准测试）和1.7至2.0倍（第二个基准测试），表明运行间可靠性显著提升。此外，在几乎所有设置中ACFS均优于CEM-SO、SGD-CVaR和KDE-SO，消融实验与敏感性分析进一步证实所提设计的贡献与鲁棒性。