We consider the problem of determining feasible systems from a finite set of simulated alternatives with respect to probability constraints, where the observations from stochastic simulations are Bernoulli distributed. Most statistically valid procedures for feasibility determination focus on constraints on the means of normally distributed observations. Although these procedures can be adapted to Bernoulli-distributed data by treating batch means as basic observations, achieving approximate normality often requires a large batch size, potentially leading to the unnecessary waste of observations in reaching a decision. This paper proposes a procedure that utilizes the Bernoulli-distributed observations directly to determine feasibility. In addition, we incorporate subjective constraints, allowing for multiple thresholds for each constraint. We demonstrate that our proposed procedure is statistically valid and that it outperforms an existing feasibility determination procedure for subjective constraints originally developed for normally distributed observations. Furthermore, we propose two heuristic feasibility check approaches for thresholds that are sequentially added by decision makers, allowing thresholds to be tightened when many systems are feasible or relaxed when no feasible system exists. We show by experiments that the proposed procedures can efficiently provide feasibility decisions to systems with respect to all thresholds considered.

翻译：我们研究了在概率约束下，从有限个模拟备选系统中判定可行系统的问题，其中随机模拟的观测值服从伯努利分布。现有大多数具有统计有效性的可行性判定方法主要针对正态分布观测值的均值约束。虽然这些方法可通过将批量均值作为基本观测值来适配伯努利分布数据，但实现近似正态性通常需要较大的批处理规模，这可能导致在决策过程中不必要地浪费观测样本。本文提出一种直接利用伯努利分布观测值进行可行性判定的方法。此外，我们引入了主观约束机制，允许为每个约束设置多个阈值。理论证明表明，所提方法具有统计有效性，并且优于现有针对正态分布观测值的主观约束可行性判定方法。进一步地，我们针对由决策者逐步添加的阈值提出了两种启发式可行性检验策略：当大量系统可行时可收紧阈值，或当不存在可行系统时可放宽阈值。实验表明，所提方法能针对所有考虑阈值高效地为系统提供可行性决策。