Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces

• 成果类型: Article
• 署名作者: Eckman, David J.; Plumlee, Matthew; Nelson, Barry L.
• 署名单位: Texas A&M University System; Texas A&M University College Station; Northwestern University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2021.2206
• 发表日期: 2022
• 页码: 3473-3489
• 关键词: simulation optimization; screening; feasibility

摘要

When working with models that allow for many candidate solutions, simulation practitioners can benefit fromscreening out unacceptable solutions in a statistically controlled way. However, for large solution spaces, estimating the performance of all solutions through simulation can prove impractical. We propose a statistical framework for screening solutions even when only a relatively small subset of them is simulated. Our framework derives its superiority over exhaustive screening approaches by leveraging available properties of the function that describes the performance of solutions. The framework is designed to work with a wide variety of available functional information and provides guarantees on both the confidence and consistency of the resulting screening inference. We provide explicit formulations for the properties of convexity and Lipschitz continuity and show through numerical examples that our procedures can efficiently screen outmany unacceptable solutions.