Efficient Dynamic Allocation Policy for Robust Ranking and Selection under Stochastic Control Framework

Hui Xiao; Zhihong Wei

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arXiv · 2305.07603

Efficient Dynamic Allocation Policy for Robust Ranking and Selection under Stochastic Control Framework

Hui Xiao·Zhihong Wei

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Abstract

This research considers the ranking and selection with input uncertainty. The objective is to maximize the posterior probability of correctly selecting the best alternative under a fixed simulation budget, where each alternative is measured by its worst-case performance. We formulate the dynamic simulation budget allocation decision problem as a stochastic control problem under a Bayesian framework. Following the approximate dynamic programming theory, we derive a one-step-ahead dynamic optimal budget allocation policy and prove that this policy achieves consistency and asymptotic optimality. Numerical experiments demonstrate that the proposed procedure can significantly improve performance.

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Hui Xiao, Zhihong Wei. 2023-05-12. Efficient Dynamic Allocation Policy for Robust Ranking and Selection under Stochastic Control Framework. https://arxiv.org/abs/2305.07603

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