Approximate Simulation Budget Allocation for Subset Ranking
Approximate Simulation Budget Allocation for Subset Ranking
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DOI：
10.1109/tcst.2016.2539329
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发表时间：
2017
影响因子：
4.8
通讯作者：
Junqi Zhang;Zezhou Li;Cheng Wang;D. Zang;Mengchu Zhou
中科院分区：
文献类型：
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作者：
Junqi Zhang;Zezhou Li;Cheng Wang;D. Zang;Mengchu Zhou
离散事件系统性能的精确评估需要大量的仿真重复，因此是耗时和昂贵的。因此，在进行模拟时，效率始终是一个大问题。为了大大降低其成本时，进行他们，有序优化出现。为了进一步提高序优化的效率，提出了最优计算预算分配算法（OCBA），以准确、快速地确定最优设计方案。它的变体已经被引入以实现具有不同假设的目标，例如识别设计的最佳子集。它们在选择最佳设计或最优设计子集方面受到限制。然而，一个非常具有挑战性的问题，即，子集排名，仍然没有解决。它超越了最佳设计和最优子集问题。这项工作开发了一个新的基于OCBA的方法来解决这个问题，并建立其理论基础。数值测试结果表明，在适当的参数下，该方法确实可以提高模拟效率，并且在子集排序正确概率和计算效率方面优于其他现有方法。
Accurate performance evaluation of discrete event systems needs a huge number of simulation replications and is thus time-consuming and costly. Hence, efficiency is always a big concern when simulations are conducted. To drastically reduce its cost when conducting them, ordinal optimization emerges. To further enhance the efficiency of ordinal optimization, optimal computing budget allocation (OCBA) is proposed to decide the best design accurately and quickly. Its variants have been introduced to achieve goals with distinct assumptions, such as to identify the optimal subset of designs. They are restricted in selecting the best design or optimal subset of designs. However, a highly challenging issue, i.e., subset ranking, remains unaddressed. It goes beyond best design and optimal subset problems. This work develops a new OCBA-based approach to address the issue and establishes its theoretical foundation. The numerical testing results show that, with proper parameters, it can indeed enhance the simulation efficiency and outperform other existing methods in terms of the probability of correct subset ranking and computational efficiency.