In this paper, we introduce a technique to enhance the computational efficiency of solution algorithms for high-dimensional discrete simulation-based optimization problems. The technique is based on innovative adaptive partitioning strategies that partition the feasible region using solutions that has already been simulated as well as prior knowledge of the problem of interesting. We integrate the proposed strategies with the Empirical Stochastic Branch-and-Bound framework proposed by Xu and Nelson (2013). This combination leads to a general-purpose discrete simulation-based optimization algorithm that is both globally convergent and has good small sample (finite-time) performance. The proposed general-purpose discrete simulation-based optimization algorithm is validated on a synthetic discrete simulation-based optimization problem and is then used to address a real-world car-sharing fleet assignment problem. Experiment results show that the proposed strategy can increase the algorithm efficiency significantly.

翻译：本文提出一种提升高维离散仿真优化问题求解算法计算效率的技术。该技术基于创新的自适应分区策略，利用已仿真解及对目标问题的先验知识对可行域进行划分。我们将所提策略与Xu和Nelson（2013）提出的经验随机分支定界框架相结合，形成了一种通用离散仿真优化算法，该算法兼具全局收敛性与优良的小样本（有限时间）性能。所提出的通用离散仿真优化算法首先在合成离散仿真优化问题上得到验证，随后应用于实际汽车共享车队调度问题。实验结果表明，该策略能显著提升算法效率。