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.

本研究项目将研究全球按订单生产供应链中与接收、接受、处理和履行客户订单相关的业务流程。 它将调查数据库和数据流的基础上这些过程，业务决策过程和这些相互作用的方式。 将开发模拟和优化模型，以监测输入数据的实时流。 监控这些流的算法必须确定预测何时应该改变，并在适当的情况下计算这些变化。 还将研究开发动态适应传感器数据流的马尔可夫决策过程（MDP）。 除了建立在MDP研究的基础上，本主题还将考虑使用混合可编程模型来设置MDP参数。研究结果将为改进订单生产供应链的运作控制提供依据。 它将提供理论基础的“永远在”控制器，持续监控供应链数据源和项目的关键业务流程的未来状态的发展。 这项研究将支持在正常操作和事件响应下改进决策。 支持的正常操作决策可以包括产品定位决策、订单时间和接受决策以及生产调度决策。 将利用事件应对模式制定应对重大破坏性事件的战略。 此类事件可能包括重大供应运输的失败或延迟、工厂的重大故障或接受了抢占现有订单承诺的大订单。 这些能力应该大大提高客户服务水平和使用它们的供应链的盈利能力。