Journal Article
Robust ranking and selection (R&S) is an important and challenging variation of the 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, the authors 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, the authors derive a new asymptotically optimal solution to the budget allocation problem. Accordingly, the authors design a new sequential optimal computing budget allocation (OCBA) procedure to solve robust R&S problems efficiently. They then conduct a comprehensive numerical study to verify the superiority of their robust OCBA procedure over the existing ones.
The numerical study also provides insights on the budget allocation behaviors that lead to enhanced efficiency.
