New Approaches for Simulation-Based Optimal Decision Making

基于仿真的最优决策的新方法

• 批准号: 1434419
• 负责人: Michael Fu
• 金额: $ 22万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1434419&HistoricalAwards=false
• 关键词: New; Approaches; Simulation; Based; Optimal

Simulation is widely used in many industrial settings, from manufacturing and supply chain management to service systems, including health care, transportation, and financial services. Due to the complexity of many of these systems, however, computation has often been a limiting factor in solving large-scale problems based on simulation models, even with the continuing advances in computing power. This award supports fundamental research leading to new algorithms that would improve the efficiency of finding optimal decisions for many problems in the manufacturing and service industries mentioned above, and thus lead to direct benefits to the U.S. economy and society. The research involves mathematical models, computing, applied probability, and statistics. Direct gradient estimation techniques such as perturbation analysis and the likelihood ratio method provide computationally efficient methods for obtaining unbiased gradient estimators without the need for resimulation. Such estimators are the basis for gradient-based search procedures used in many simulation optimization algorithms. However, the resulting algorithms use only the gradients, consistent with their application in the deterministic optimization setting, where the gradients are exact so there is no value gained in using the objective function (or performance measure) values themselves for performing gradient search. On the other hand, in the stochastic setting, the gradient estimates are noisy, which means that using the function values to provide additional information on estimating the gradient may be beneficial. The proposed research explores new methods for incorporating direct gradient estimates from stochastic simulation into existing simulation optimization techniques, specifically response surface methodology and stochastic approximation. The goals of the research include: (i) developing new more effective algorithms, (ii) proving convergence of the resulting algorithms, (iii) analyzing finite-time properties of the algorithms, and (iv) providing practical implementation guidelines based on both theory and empirical numerical testing. Thus, in addition to algorithmic advances, new theory will likely be needed to provide guidance as to the settings in which the new algorithms are likely to provide additional benefit.

从制造和供应链管理到服务系统，包括医疗保健，运输和金融服务，模拟广泛用于许多工业环境中。 但是，由于许多这些系统的复杂性，即使计算能力的持续进展，计算通常是解决大规模问题的限制因素。 该奖项支持基础研究，导致新算法，这些算法将提高为上述制造业和服务行业中许多问题找到最佳决策的效率，从而导致对美国经济和社会的直接利益。 该研究涉及数学模型，计算，应用概率和统计数据。 直接梯度估计技术（例如扰动分析）和似然比方法提供了计算有效的方法，用于获取无偏梯度估计器，而无需重复拟合。 此类估计器是许多模拟优化算法中使用的基于梯度的搜索程序的基础。 但是，所得算法仅使用梯度，与确定性优化设置中的应用相一致，其中梯度精确，因此在使用目标函数（或性能度量）值本身以进行梯度搜索时，没有获得值。 另一方面，在随机环境中，梯度估计值很嘈杂，这意味着使用功能值提供有关估计梯度的其他信息可能是有益的。拟议的研究探讨了将随机模拟中直接梯度估计的新方法纳入现有的仿真优化技术，特别是响应表面方法论和随机近似。该研究的目标包括：（i）开发新的更有效的算法，（ii）证明所得算法的融合，（iii）分析算法的有限时间属性，以及（iv）基于理论和经验数量测试的实际实施指南。因此，除了算法进步外，还可能需要新理论来为新算法可能提供额外好处的环境提供指导。