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.

该赠款为开发一个新型框架提供了资金，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在上述环境中遇到的总体资源分配问题的分解为两个主要的子问题：这些子问题中的第一个被称为目标环境的逻辑或行为控制问题，它试图防止系统行为的问题或不良模式的发展。解决此子问题的方法基础是定性离散事件系统（DES）理论提供的结果。第二个子问题被称为绩效控制或调度问题，它试图优化逻辑控制器提供的行为纬度中所选的某些性能索引，该逻辑控制器由第一个子问题返回。马尔可夫决策过程（MDP）和其中的一些最新发展（共同称为近似动态编程（ADP））提供了第二个子问题的方法论基础。在拟议的调查中，特别强调了通常是上述问题基础的非常高的计算复杂性，而所追求的解决方案将寻求提供明确的杠杆，以使系统设计师能够系统地折衷计算和运营效率。最后，提出的理论发展将通过将其应用于（i）（i）灵活自动化生产单元的吞吐量最大化以及（ii）工业自动化指导车辆（AGV）系统的问题。通过这种方式，它们将实现并促进经常为许多当代应用所设想的并发性和操作灵活性，但实际上由于当前无法掌握源于这些概念的操作复杂性而受到限制。在更加分析的层面上，拟议的研究将进一步促进ADP的新兴领域，它将确定并追求该领域与定性理论之间的新有趣的协同作用。最后，在教育方面，拟议的计划将在研究生工程课程中促进和加强DES和ADP理论的存在，并通过积极参与追求研究的积极参与，为许多研究生提供机会，从而为许多研究生提供机会。