EAGER: Cybermanufacturing: Design of an Agile and Smart Manufacturing Exchange: Enabling Small Businesses through Standardized Protocols and Distributed Optimization

EARLY概念探索性研究资助（EAGER）奖支持关于敏捷制造交换系统（MES）设计的基础研究，其中原材料供应商，装配商，运输公司等，将通过标准化协议参与，以完成复杂的制造订单。此设计将为智能软件中介层（即，“代理”），其将使MES能够自学习并适应于动态/多样化的服务请求和资源可用性，以及支持复杂信息生态系统内的服务提供商和用户的大型网络。美国的经济竞争力取决于制造业智能大规模定制系统的创新方法，这将使小型和多样化订单的处理几乎立即完成。MES将通过支持按需整合资源、从故障中优雅恢复以及动态适应而不会中断运营来实现这一转变。 为了实现这些目标，研究将集中在通过实时动态演变优化策略来适应新出现的系统行为。用户和供应商将连接在一个动态的制造网络中，该网络将适应多种产品流，供应商和他们自己之间的链接的不确定性，以及在网络组件出现故障的情况下提供服务的容错能力。这种适应性、无缝效率和不间断服务的水平将是迈向智能MES的重要一步。研究目标将通过设计一个分布式实时优化和知识发现框架来实现，该框架将在用户，经纪人和供应商的动态制造网络中解决工作流优化，资源分配和数据驱动的性能预测。具体的研究任务包括在线准入控制策略，动态生产计划，分析和预测服务水平的性能预测，分布式方法的动态资源分配下的不确定性，和可视化分析技术，以支持人类决策者和态势感知。

This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research on the design of an agile manufacturing exchange system (MES) in which suppliers of raw materials, assemblers, transportation companies, etc., will participate through standardized protocols to fulfill complex manufacturing orders. This design will provide the foundation for a smart software mediation layer (i.e., a "broker") that will enable a MES to be self-learning and adaptive to dynamic/diverse service requests and resource availability, as well as support a large network of service providers and users within a complex information ecosystem. The economic competitiveness of the U.S. dependss on new and innovative methods for intelligent mass customization systems for the manufacturing sector, which will enable the processing of small-sized and diverse orders that demand almost instant fulfillment. The MES will enable this transformation by supporting on-demand integration of resources, graceful recovery from failures, and dynamic adaptation without any disruption in operations. In order to meet these goals, research will be focused on adaptation to emerging system behaviors by dynamically evolving optimization strategies in real-time. Users and providers will be connected in a dynamic manufacturing network that will accommodate multiple product flows, uncertainty in links between providers and themselves, and fault tolerance to provide service despite failed network components. This level of adaptation, seamless efficiency, and uninterrupted service will constitute a significant step forward towards a smart MES. The research goals will be accomplished through the design of a distributed real-time optimization and knowledge discovery framework that will address workflow optimization, resource allocation, and data-driven performance prediction in a dynamic manufacturing network of users, brokers, and providers. The specific research tasks include online admission control policies, dynamic production planning, analysis and prediction of service-level performance for forecasting, distributed methods for dynamic resource allocation under uncertainty, and visual analytics techniques to support human decision makers and situational awareness.