Collaborative Research: Design Principles for Parallel Simulation Optimization

协作研究：并行仿真优化的设计原理

• 批准号: 1200315
• 负责人: Shane Henderson
• 金额: $ 25.05万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1200315&HistoricalAwards=false
• 关键词: Collaborative; Research; Design; Principles; Parallel

This award provides funding to advance the design of algorithms for solving simulation-optimization (SO) problems on parallel computing platforms. SO problems are optimization problems where the objective function and constraints can only be observed through a stochastic simulation. Virtually all current algorithms for solving SO problems assume a single processor as the computing platform. However, the trend in computing devices is towards multi-processor computers, not just at the laptop/desktop level, but all the way up to cloud computing environments. This research will explore how to design algorithms for solving SO problems that exploit such environments, to attempt to return high-quality solutions at a reasonable cost and within a reasonable amount of time.If successful, the results of this research will lead to a new line of research - parallel SO - with ensuing improvements in the design and implementation of SO algorithms on parallel computing platforms, thus making this currently computing-intensive technology much more accessible and effective. SO already holds an important place in application fields, as evidenced by the variety of SO problems in an existing testbed www.simopt.org. The proposed research will provide further opportunities for major impact through reliable solution of important SO problems. This research is part of a continuing thrust to enhance the implementability of SO by creating methods, theory, and computational tools to facilitate the automatic solution of larger and more realistic SO problems.

该奖项提供资金，以推进在并行计算平台上解决模拟优化（SO）问题的算法设计。SO问题是一种优化问题，其中目标函数和约束只能通过随机模拟来观察。目前求解SO问题的算法几乎都是以单处理器为计算平台的。然而，计算设备的趋势是朝向多处理器计算机，不仅在膝上型计算机/台式机级别，而且一直到云计算环境。本研究将探索如何设计算法来解决SO问题，利用这种环境，试图返回高质量的解决方案，在一个合理的成本和合理的时间。如果成功的话，这项研究的结果将导致一个新的研究路线-并行SO -随之而来的改进设计和实现的SO算法在并行计算平台上，从而使得这种当前计算密集型的技术更加容易获得和有效。SO已经在应用领域中占有重要的地位，证明了各种SO问题在现有的测试平台www.simopt.org。拟议的研究将提供进一步的机会，通过可靠的解决方案，重要的SO问题的重大影响。这项研究是一个持续的推力，以提高SO的可实施性，通过创建方法，理论和计算工具，以促进更大，更现实的SO问题的自动解决方案的一部分。