The Local Time Method for Targeting and Selection

• 成果类型: Article
• 署名作者: Ryzhov, Ilya O.
• 署名单位: University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2018.1731
• 发表日期: 2018
• 页码: 1406-1422
• 关键词: simulation budget allocation; ranking

摘要

We propose a framework for targeting and selection (T&S), a new problem class in simulation optimization where the objective is to select a simulation alternative whose mean performance matches a prespecified target as closely as possible. T&S resembles the more well-known problem of ranking and selection but presents unexpected challenges: for example, a one-step look-ahead method may produce statistically inconsistent estimates of the values, even under very standard normality assumptions. We create a new and fundamentally different approach, based on a Brownian local time model, that exhibits characteristics of two widely studied methodologies, namely expected value of information and optimal computing budget allocation. We characterize the asymptotic sampling rates of this method and relate them to the convergence rates of metrics of interest. The local time method outperforms benchmarks in experiments, including problems where the modeling assumptions of T&S are violated.