Robust ranking and selection (R&S) is an important and challenging variation of conventional R&S that seeks to select the best alternative among a finite set of alternatives. It captures the common input uncertainty in the simulation model by using an ambiguity set to include multiple possible input distributions and shifts to select the best alternative with the smallest worst-case mean performance over the ambiguity set. In this paper, we aim at developing new fixed-budget robust R&S procedures to minimize the probability of incorrect selection (PICS) under a limited sampling budget. Inspired by an additive upper bound of the PICS, we derive a new asymptotically optimal solution to the budget allocation problem. Accordingly, we design a new sequential optimal computing budget allocation (OCBA) procedure to solve robust R&S problems efficiently. We then conduct a comprehensive numerical study to verify the superiority of our robust OCBA procedure over existing ones. The numerical study also provides insights on the budget allocation behaviors that lead to enhanced efficiency.

翻译：鲁棒排序与选择（R&S）是传统R&S中一个重要且具有挑战性的变体，其目标是从有限备选方案中选出最优方案。该方法通过引入模糊集来涵盖多种可能的输入分布，从而捕捉仿真模型中常见的输入不确定性，并转向选择在模糊集上具有最小最坏情况均值性能的最优方案。本文旨在开发新的固定预算鲁棒R&S方法，以在有限采样预算下最小化错误选择概率（PICS）。受PICS加法上界的启发，我们推导出预算分配问题的一种新的渐近最优解。基于此，我们设计了一种新的序列最优计算预算分配（OCBA）方法，以高效求解鲁棒R&S问题。随后，我们进行了全面的数值研究，验证了所提出的鲁棒OCBA方法相较于现有方法的优越性。数值研究还揭示了提升效率的预算分配行为机制。