This paper is concerned with the efficient estimation of the risk measure of a system where the estimation requires solving a two-stage simulation optimization problem. The first stage samples risk factors that specify a second stage simulation optimization problem. The second stage solves a simulation optimization problem and outputs the best performance of the system under the realized risk factors, which are then aggregated across all first stage samples to produce an estimate of the risk measure. Applications of such an estimation scheme arise frequently in important industries such as financial, healthcare, logistics, and manufacturing. Because a large number of first stage samples are typically needed, each of which requires solving a computationally expensive simulation optimization problem, the two-stage simulation optimization approach faces a major computational efficiency challenge. In response to this challenge, this paper proposes a sequential simulation budget allocation procedure that determines the allocation of simulation budget based on a score known as revised probability of sign change for each decision under each scenario. The consistency of the proposed procedure is proved and the computational efficiency gain of the proposed is demonstrated using both benchmark test functions and two test cases in the context of financial portfolio risk estimation and healthcare system resilience estimation.

本文关注系统风险度量的有效估计，其中估计需要解决两阶段模拟优化问题。第一阶段对指定第二阶段模拟优化问题的风险因素进行抽样。第二阶段解决模拟优化问题并输出系统在已实现风险因素下的最佳性能，然后将所有第一阶段样本汇总以产生风险度量的估计。这种估算方案的应用在金融、医疗保健、物流和制造等重要行业中频繁出现。因为通常需要大量的第一阶段样本，每个样本都需要解决计算量大的模拟优化问题，两阶段模拟优化方法面临着计算效率方面的重大挑战。为了应对这一挑战，本文提出了一种顺序模拟预算分配程序，该程序根据每个场景下每个决策的符号变化修订概率来确定模拟预算的分配。在金融投资组合风险估计和医疗保健系统弹性估计的背景下，使用基准测试函数和两个测试用例证明了所提出程序的一致性，并证明了所提出的计算效率增益。本文提出了一种顺序模拟预算分配程序，该程序根据每个场景下每个决策的符号变化修正概率得分来确定模拟预算的分配。在金融投资组合风险估计和医疗保健系统弹性估计的背景下，使用基准测试函数和两个测试用例证明了所提出程序的一致性，并证明了所提出的计算效率增益。本文提出了一种顺序模拟预算分配程序，该程序根据每个场景下每个决策的符号变化修正概率得分来确定模拟预算的分配。在金融投资组合风险估计和医疗保健系统弹性估计的背景下，使用基准测试函数和两个测试用例证明了所提出程序的一致性，并证明了所提出的计算效率增益。