Optimized Scheduling of Complex Resource Allocation Systems through Approximate Dynamic Programming

通过近似动态规划复杂资源分配系统的优化调度

• 批准号: 0928231
• 负责人: Spiridon Reveliotis
• 金额: $ 34.11万
• 依托单位: Georgia Tech Research Corporation
• 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: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0928231&HistoricalAwards=false
• 关键词: Optimized; Scheduling; Complex; Resource; Allocation

This grant provides funding for the development of a novel framework for managing the complex resource allocation that takes place in contemporary production and service systems. A defining characteristic of this framework is the decomposition of the overall resource allocation problem encountered in the aforementioned environments into two major sub-problems: The first of these sub-problems is known as the Logical or Behavioral Control problem for the target environments, and it seeks to prevent the development of problematic or undesirable patterns in the system behavior. The methodological base for addressing this sub-problem is a body of results provided by Qualitative Discrete Event Systems (DES) theory. The second sub-problem is known as the Performance Control or Scheduling problem, and it seeks to optimize some chosen performance indices within the behavioral latitude provided by the logical controller that is returned by the first sub-problem. The methodological base for this second sub-problem is provided by the theory of Markov Decision Processes (MDP) and some more recent developments in it, collectively known as Approximate Dynamic Programming (ADP). In the proposed investigations, special emphasis is placed on the very high computational complexity that typically underlies the aforementioned problems, and the pursued solutions will seek to provide explicit levers that will enable the system designers to systematically trade-off computational and operational efficiencies. Finally, the proposed theoretical developments will be concretized by applying them to the problems of throughput maximization of (i) flexibly automated production cells and (ii) industrial Automated Guided Vehicle (AGV) systems.If successful, the results of this research will bring closer the existing developments in scheduling theory to the field practice, since they will help to deal more effectively with the underlying complexities. In this way, they will enable and promote the concurrency and the operational flexibilities that have been frequently envisioned for many contemporary applications, but have been limited in practice by the current inability to master the operational complexity that stems from these concepts. At a more analytical level, the proposed research will further promote the burgeoning area of ADP and it will identify and pursue new interesting synergies between this area and Qualitative DES theory. Finally, on the educational side, the proposed program will promote and strengthen the presence of the DES and ADP theories in the graduate engineering curriculum, and it will give the opportunity to a number of graduate students to experience the potential of these theories and their results, through active participation in the pursued research.

这笔赠款为开发一个新颖的框架提供资金，用于管理当代生产和服务系统中发生的复杂资源分配。该框架的一个定义特征是将上述环境中遇到的总体资源分配问题分解为两个主要子问题：第一个子问题称为目标环境的逻辑或行为控制问题，它旨在防止系统行为中出现有问题或不良模式。解决这个子问题的方法基础是定性离散事件系统 (DES) 理论提供的一系列结果。第二个子问题称为性能控制或调度问题，它寻求在第一个子问题返回的逻辑控制器提供的行为范围内优化某些选定的性能指标。第二个子问题的方法基础是由马尔可夫决策过程 (MDP) 理论及其最新发展提供的，统称为近似动态规划 (ADP)。在拟议的研究中，特别强调通常构成上述问题的非常高的计算复杂性，所追求的解决方案将寻求提供明确的杠杆，使系统设计者能够系统地权衡计算和操作效率。最后，所提出的理论发展将通过应用于（i）灵活自动化生产单元和（ii）工业自动导引车（AGV）系统的吞吐量最大化问题来具体化。如果成功，这项研究的结果将更接近将调度理论的现有发展应用于现场实践，因为它们将有助于更有效地处理潜在的复杂性。通过这种方式，它们将实现并促进许多当代应用程序经常设想的并发性和操作灵活性，但在实践中由于当前无法掌握源自这些概念的操作复杂性而受到限制。在更具分析性的层面上，拟议的研究将进一步促进 ADP 的新兴领域，并将确定并追求该领域与定性 DES 理论之间新的有趣的协同作用。最后，在教育方面，拟议的计划将促进和加强 DES 和 ADP 理论在研究生工程课程中的存在，并将为许多研究生提供体验这些理论及其结果的潜力的机会，通过积极参与所追求的研究。