Collaborative Research: Combining Gradient and Adaptive Search in Simulation Optimization

协作研究：在仿真优化中结合梯度和自适应搜索

• 批准号: 0900332
• 负责人: Jiaqiao Hu
• 金额: $ 14.99万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0900332&HistoricalAwards=false
• 关键词: Collaborative; Research; Combining; Gradient; Adaptive

"Combining Gradient and Adaptive Search in Simulation Optimization" This research project aims to make significant theoretical and practical advances in simulation optimization. Specifically, we plan on doing the following: (i) develop new simulation optimization algorithms based on different sequences of the so-called "reference distributions" in a recently developed approach called model reference adaptive search, and new hybrid global-local search algorithms integrating local gradient search and problem structure; and (ii) conduct rigorous theoretical analysis of the resulting algorithms, both finite-time behavior using an adaptive search framework and asymptotic behavior using a novel connection to stochastic approximation methods. We will also develop efficient computational selection methods for implementing these algorithms in simulation optimization, where the objective function requires multiple simulation replications, which are computationally expensive, in order to estimate system performance. A wide variety of applications from supply chain management to financial engineering will be tested for the purposes of investigating specific gradient search algorithms and problem structure, and evaluating the effectiveness in terms of empirical behavior. Simulation is used throughout the US industry, so if successful, the resulting optimization algorithms will have broad practical applicability. To attack difficult problems arising from large, complex stochastic discrete-event simulation models will require significant new methodologies, leading to research advances in both algorithmic development and convergence analysis. In terms of theory, the rigorous analysis will explore connections to a rich body of results in stochastic approximation and stochastic adaptive research that have never been employed in this manner before, yielding new insights into both finite-time performance and asymptotic rates of convergence. In terms of practice, this line of research fills an important part of the "analytics" computational tool kit that has led to increased competitiveness for US businesses from manufacturers and retailers with global supply chains to financial services managing complex risk factors.

该研究项目“将梯度和自适应搜索结合在模拟优化中”旨在在模拟优化方面取得重大理论和实际进步。具体而言，我们计划执行以下操作：（i）在最近开发的一种称为模型参考自适应搜索的方法中，基于所谓的“参考分布”的不同序列开发新的仿真优化算法，以及新的混合全球 - 本地搜索算法集成本地梯度搜索和问题结构； （ii）对所得算法进行严格的理论分析，既有使用自适应搜索框架的有限时间行为，又是使用与随机近似方法的新连接的渐近行为进行的。 我们还将开发有效的计算选择方法，用于在模拟优化中实现这些算法，在该算法中，目标函数需要多个模拟复制，这些复制在计算上昂贵，以估计系统性能。 从供应链管理到金融工程的各种应用将进行测试，以研究特定的梯度搜索算法和问题结构，并评估经验行为的有效性。 在整个美国行业中都使用了仿真，因此，如果成功，则由此产生的优化算法将具有广泛的实际适用性。要攻击由大型，复杂的随机离散事件模拟模型引起的困难问题，将需要重大的新方法，从而导致算法开发和收敛分析的研究进展。 就理论而言，严格的分析将探索与以前从未以这种方式使用的随机近似和随机自适应研究的相关性的联系，从而为有限的时间性能和渐近融合率提供了新的见解。 在实践方面，这一研究范围填补了“分析”计算工具套件的重要组成部分，该工具套件从拥有全球供应链的制造商和零售商到管理复杂风险因素的金融服务的美国企业提高了竞争力。