Efficient Dynamic Simulation Allocation in Ordinal Optimization
Efficient Dynamic Simulation Allocation in Ordinal Optimization
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DOI：
10.1109/tac.2006.884993
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发表时间：
2006-12
影响因子：
6.8
通讯作者：
Chun-Hung Chen;Donghai He;M. Fu
中科院分区：
文献类型：
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作者：
Chun-Hung Chen;Donghai He;M. Fu
有序优化是一种有效的仿真优化方法。合理分配不同设计的仿真样本可以进一步显著提高有序优化的效率。通过比较顺序版本的最优计算预算分配（OCBA）方法与最优静态和一步前瞻动态分配方案在抽样分布上的“完美信息”，研究了使用动态模拟分配进行有序优化的效率增益。计算结果表明，这种基于估计性能的顺序版本的OCBA可以轻松优于使用真实抽样分布导出的最优静态分配。这些结果表明，在确定良好的模拟预算分配时，顺序分配的优势往往超过对均值和方差的准确估计。此外，完美信息动态方案的性能可以看作是不同顺序方案性能的近似上界，从而为利用动态分配进一步实现效率提高提供了一个目标
Ordinal optimization has emerged as an efficient technique for simulation optimization. A good allocation of simulation samples across designs can further dramatically improve the efficiency of ordinal optimization. We investigate the efficiency gains of using dynamic simulation allocation for ordinal optimization by comparing the sequential version of the optimal computing budget allocation (OCBA) method with optimal static and one-step look-ahead dynamic allocation schemes with "perfect information" on the sampling distribution. Computational results indicate that this sequential version of OCBA, which is based on estimated performance, can easily outperform the optimal static allocation derived using the true sampling distribution. These results imply that the advantage of sequential allocation often outweighs having accurate estimates of the means and variances in determining a good simulation budget allocation. Furthermore, the performance of the perfect information dynamic scheme can be viewed as an approximate upper bound on the performance of different sequential schemes, thus providing a target for further achievable efficiency improvements using dynamic allocations