New Additive OCBA Procedures for Robust Ranking and Selection

TL;DR

This paper introduces new additive OCBA procedures for robust ranking and selection, effectively handling input uncertainty and optimizing budget allocation to improve decision accuracy under limited sampling.

Contribution

It develops a novel asymptotically optimal solution and sequential OCBA procedure for robust R&S, addressing input uncertainty with a new additive upper bound approach.

Findings

Proposed robust OCBA outperforms existing methods in numerical tests.

New budget allocation insights improve efficiency in robust R&S.

Method effectively minimizes incorrect selection probability under limited budgets.

Abstract

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.