DDDAS-SMRP: Dynamic Real-Time Order Promising and Fulfillment for Global Make-to-Order Supply Chains

DDDAS-SMRP：全球按单生产供应链的动态实时订单承诺和履行

• 批准号: 0540312
• 负责人: Michael Ball
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540312&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Dynamic; Real; Time

This research project will study the business processes related to receiving, accepting, processing and fulfilling customer orders in global make-to-order supply chains. It will investigate the databases and data flows underlying these processes, the business decision processes and the manner in which these interact. Simulation and optimization models will be developed that monitor real-time streams of input data. Algorithms that monitor these streams must determine when predictions should change and should calculate such changes if appropriate. Research will also be carried out into the development Markov Decision Processes (MDPs) that dynamically adapt to sensor data streams. In addition to building on MDP research, this topic will consider the use of Mixed Integer Programming models for setting MDP parameters. The results of this research will provide the basis for improved operational control of make-to-order supply chains. It will provide the theory underlying the development of "always on" controllers that continuously monitor supply chain data sources and project the future status of key business processes. This research will support improved decision making under both normal operations and incident response. The normal operations decisions supported could include product positioning decisions, order timing and acceptance decisions and production scheduling decisions. The incident response mode will be used to develop a strategy for reacting to major disruptive events. Such events might include the failure or delay of a major supply shipment, a major breakdown in a factory or acceptance of a large order that preempts existing order commitments. These capabilities should substantially increase the level of customer service and the profitability for supply chains that employ them.

该研究项目将研究与全球订购供应链中接收，接受，处理和实现客户订单有关的业务流程。 它将研究这些过程，业务决策过程以及这些交互方式的数据库和数据流。 将开发模拟和优化模型，以监视输入数据的实时流。 监视这些流的算法必须确定何时应更改预测，并应在适当的情况下计算此类更改。 研究还将在动态适应传感器数据流的开发马尔可夫决策过程（MDP）中进行。 除了在MDP研究上进行构建外，该主题还将考虑使用混合整数编程模型来设置MDP参数。这项研究的结果将为改善订购供应链的运营控制提供基础。 它将提供“始终在”控制器上开发的理论，该理论不断监视供应链数据源并投影关键业务流程的未来状态。 这项研究将支持在正常操作和事件响应下改进决策。 所支持的正常操作决策可能包括产品定位决策，订单时机和接受决策以及生产计划决策。 事件响应模式将用于制定对重大破坏性事件反应的策略。 此类事件可能包括大量供应运输的失败或延迟，工厂的重大细分或接受大量抢先订单承诺的大订单。 这些功能应大大提高客户服务水平以及雇用其供应链的盈利能力。