Intelligent simulation for alternatives comparison and application to air traffic management
Intelligent simulation for alternatives comparison and application to air traffic management
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DOI：
10.1007/s11518-006-0180-0
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发表时间：
2005-03
影响因子：
1.2
通讯作者：
Chun-Hung Chen;Donghai He
中科院分区：
文献类型：
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作者：
Chun-Hung Chen;Donghai He
我们提出了一种模拟运行分配方案，以提高不确定性下决策模拟实验的效率。该方案称为最优计算预算分配（OCBA）。 OCBA 通过智能地将计算预算分配给正在评估的候选替代方案来推进最先进的技术。基本思想是花费更少的计算工作来模拟非关键替代方案，以节省计算成本。特别是，OCBA 用于智能地提供最少数量的模拟运行以获得所需的精度。在本文中，我们提出了一种新的、更通用的 OCBA 方案，该方案可以考虑用户不仅对最佳设计感兴趣，而且对良好设计集中的任何一个感兴趣的情况。此外，本文还介绍了我们的 OCBA 在美国空中交通管理设计问题中的应用。美国的国家空中交通系统被建模为一个大型、复杂和随机的网络。数值例子表明，使用OCBA可以将计算时间减少54%到88%。
We present a simulation run allocation scheme for improving efficiency in simulation experiments for decision making under uncertainty. This scheme is called Optimal Computing Budget Allocation (OCBA). OCBA advances the state-of-the-art by intelligently allocating a computing budget to the candidate alternatives under evaluation. The basic idea is to spend less computational effort on simulating non-critical alternatives to save computation cost. In particular, OCBA is employed to intelligently provide the smallest number of simulation runs for a desired accuracy. In this paper, we present a new and more general OCBA scheme which can consider cases that users are interested not only the best design, but also any one in a good design set. In addition, this paper also presents the application of our OCBA to a design problem in US air traffic management. The national air traffic system in US is modeled as a large, complex, and stochastic network. The numerical examples show that the computation time can be reduced by 54% to 88% with the use of OCBA.