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Efficient Learning for Clustering and Optimizing Context-Dependent Designs

Author

Listed:
  • Haidong Li

    (College of Engineering, Peking University, Beijing 100871, China)

  • Henry Lam

    (Department of Industrial Engineering and Operations Research, Columbia University, New York 10027)

  • Yijie Peng

    (Department of Management Science and Information Systems, Guanghua School of Management, Peking University, Beijing 100871, China)

Abstract

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.

Suggested Citation

  • Haidong Li & Henry Lam & Yijie Peng, 2024. "Efficient Learning for Clustering and Optimizing Context-Dependent Designs," Operations Research, INFORMS, vol. 72(2), pages 617-638, March.
    • Handle: RePEc:inm:oropre:v:72:y:2024:i:2:p:617-638
    DOI: 10.1287/opre.2022.2368
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