Efficient Learning for Clustering and Optimizing Context-Dependent Designs

• 成果类型: Article
• 署名作者: Li, Haidong; Lam, Henry; Peng, Yijie
• 署名单位: Peking University; Columbia University; Peking University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2368
• 发表日期: 2024
• 关键词: simulation; ranking and selection; context; performance clustering

摘要

We consider a simulation optimization problem for context-dependent decision making. Under a Gaussian mixture model-based Bayesian framework, we develop a dynamic sampling policy to maximize the worst-case probability of correctly selecting the best design over all contexts, which utilizes both global clustering information and local performance information. In particular, we design a computationally efficient approximation method to learn these sources of information, thereby leading to an implementable dynamic sampling policy. The proposed sampling policy is proved to be consistent and achieve the asymptotically optimal sampling ratio. Numerical experiments show that the proposed approximation method makes a good balance between the performance and complexity, and the proposed sampling policy significantly improves the efficiency in context-dependent simulation optimization.