In “A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation,” Currie and Monks propose an algorithm for multi-objective simulation-based optimization. In contrast to sequential ranking and selection schemes, their algorithm follows a two-stage scheme. The approach is evaluated by comparing the results to those obtained using the existing OCBA-m algorithm for synthetic problems and for a hospital ward configuration problem. The authors provide the Python code used in the experiments in the form of Jupyter notebooks. The code successfully reproduced the results shown in the article.

中文翻译：

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“通过仿真进行多目标优化的子集选择的实用方法”的复制计算结果 (RCR) 报告

在“A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation”中，Currie 和 Monks 提出了一种基于多目标仿真的优化算法。与顺序排序和选择方案相比，他们的算法遵循两阶段方案。通过将结果与使用现有 OCBA-m 算法获得的结果进行比较来评估该方法，以解决综合问题和医院病房配置问题。作者以 Jupyter 笔记本的形式提供了实验中使用的 Python 代码。代码成功复现了文章中显示的结果。