SGER: Closed-loop Planning and Control of Production Systems under Uncertainties

这项探索性研究小额赠款（SGER）为闭环规划和控制系统的新但未经测试的框架提供资金，重点是分布式环境中的动态信息收集，优化和控制。新的框架将把开环规划的范例转变为闭环规划的新范例，并将创建分布式的、可扩展的系统，这些系统可以由高吞吐量计算支持，并对动态变化的环境作出真实的响应，其中存在不确定性和不完整的信息。嵌套分区优化方法将在这个框架中的应用进行研究，因为它结合了自适应全局采样与局部启发式搜索，具有多个任务，可以分布，并在并行度灵活。要探讨的一个关键方面将是动态演变的邻居解耦的分布式优化和共生进化的传感器，测量动态变化的耦合和不确定性在分布式环境中。知识耦合的完整性和信息共享的一致性的措施将开发，促进适应不确定性在分布式优化。这项工作将解决日益增长的需求，快速响应，可扩展性和适应性的规划应用程序和动态连接的规划与控制，同时探索集成的理论和方法的生产系统工程与控制系统工程。该研究将侧重于生产规划，但其结果预计将对医疗保健和交通等其他领域的动态规划具有重要意义，从而更有效地利用资源。这项工作也将有助于分布式仿真，分布式优化，分布式控制及其集成的进步。

This Small Grant for Exploratory Research (SGER) provides funding for a new but untested framework for closed-loop planning and control systems, focusing on dynamic information gathering, optimization and control in a distributed environment. The new framework would transform the paradigm of open-loop planning to a new paradigm of closed-loop planning and would create distributed, extensible systems that can be supported by high throughput computing and are responsive in real time to dynamically changing environments in which there is uncertainty and incomplete information. The nested partitions optimization method will be studied for application in this framework because it combines adaptive global sampling with local heuristic searches, has multiple tasks that can be distributed, and is flexible in the degree of parallelism. A key aspect to be explored will be dynamic evolution of neighborhoods for decoupling in distributed optimization and the symbiotic evolution of sensors that measure dynamically changing coupling and uncertainty in a distributed environment. Measures of completeness of knowledge coupling and consistency of information sharing will be developed that promote accommodation of uncertainty in distributed optimization.This work will address the growing need for quick response, extensibility and adaptability in planning applications and the dynamic linking of planning with control, while exploring the integration of the theory and methods of production systems engineering with those of control systems engineering. The research will be focused on production planning, but the results are expected to be important in dynamic planning in other areas such as health care and transportation, resulting in more efficient use of resources. The work will also contribute to advances in distributed simulation, distributed optimization, distributed control and their integration.