We describe a practical two-stage algorithm, BootComp, for multi-objective optimization via simulation. Our algorithm finds a subset of good designs that a decision-maker can compare to identify the one that works best when considering all aspects of the system, including those that cannot be modeled. BootComp is designed to be straightforward to implement by a practitioner with basic statistical knowledge in a simulation package that does not support sequential ranking and selection. These requirements restrict us to a two-stage procedure that works with any distributions of the outputs and allows for the use of common random numbers. Comparisons with sequential ranking and selection methods suggest that it performs well, and we also demonstrate its use analyzing a real simulation aiming to determine the optimal ward configuration for a UK hospital.

中文翻译：

---

通过仿真进行多目标优化子集选择的实用方法

我们描述了一种实用的两阶段算法 BootComp，用于通过仿真进行多目标优化。我们的算法找到了一个好的设计子集，决策者可以比较这些设计子集，以确定在考虑系统的所有方面时效果最好的设计，包括那些无法建模的设计。BootComp 旨在由具有基本统计知识的从业者在不支持顺序排名和选择的模拟包中直接实施。这些要求将我们限制在一个两阶段的过程中，该过程适用于输出的任何分布，并允许使用常见的随机数。与顺序排序和选择方法的比较表明它表现良好，