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A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems

Author

Listed:
  • Timothy Tay

    (Institute for Infocomm Research (I 2 R), Agency for Science, Technology and Research (A*STAR), Singapore 138632)

  • Carolina Osorio

    (Department of Decision Sciences, HEC Montréal, Montreal, Quebec H3T 2A7, Canada)

Abstract

When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms.

Suggested Citation

  • Timothy Tay & Carolina Osorio, 2024. "A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems," Transportation Science, INFORMS, vol. 58(5), pages 947-972, September.
  • Handle: RePEc:inm:ortrsc:v:58:y:2024:i:5:p:947-972
    DOI: 10.1287/trsc.2023.0110
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