Scalable Enterprise Systems Phase II: Real-Time Promising for Authority Domains Operating in a Build-to-Order Mode

这项可扩展企业系统第二阶段拨款用于研究面向动态订单到达的离散按订单生产环境的实时订单承诺的可扩展技术。本项目中将要开发的计算到期日的算法准确地考虑了当前时间分段的资源和材料的可用性、先前订单承诺以及当前系统状态。各种备选方案(资源路径或材料来源)的存在产生了组合复杂性。为了提高性能，有希望的算法将结合(1)通过研究开发的理论上合理的启发式技术，(2)最短路径等可扩展的优化算法，以及(3)计算技术。要研究的启发式方法之一是基于一个新的、甚至是有争议的想法--为了承诺的目的，可以通过只考虑部分排序的任务计划来足够准确地估计资源可用于处理操作的时间。目前对这一原则的支持是基于实践经验，但几乎没有科学证据。这个项目的一个关键原则是，结果是高度可扩展的。因此，在工业工程和计算机科学的交叉点进行联合研究在这个项目中既是必要的，也是协同的，因为当在大规模系统环境中进行研究时，有希望的和先进的计算方法的算法方面是交织在一起的。算法将在面向对象、事件驱动、内存驻留、多线程架构中实现，以进行详细研究和经验评估。最重要的短期客户服务决策之一是对客户或业务合作伙伴的请求做出准确的承诺。令人惊讶的是，这方面的研究非常少。虽然有大量文献研究如何在规定的预产期内完成预产期安排，但很少有工作涉及如何分配紧迫但可实现的预产期。这项研究的结果将提高做出这些到期日承诺的准确性和速度，这反过来将对收入、运营费用和客户满意度产生非常重要的影响。这项研究直接适用于越来越多地通过互联网直接向客户销售定制产品的制造商，以及在动态重组的商业伙伴之间自由开展协作商务的未来。

This Scalable Enterprise Systems Phase II grant funds an investigation of scalable techniques for real-time order promising by discrete build-to-order environments facing dynamic order arrivals. The algorithms to be developed in this project for calculating due dates accurately consider current time-phased availability of resources and materials, prior order commitments, and the current system state. The presence of various alternates (resource paths or material sources) creates combinatorial complexity. To increase performance, the promising algorithms will integrate (1) theoretically sound heuristic techniques developed through research, (2) scalable optimal algorithms such as shortest path, and (3) computational technology. One of the heuristics to be examined is based on a novel, even controversial, idea-- for the purposes of promising, the time when a resource will be available to process an operation can be estimated with sufficient accuracy by considering only a partially ordered task plan. Current support for this principle is based on practical experience but little scientific evidence. A key tenet of this project is that the results be highly scalable. Consequently, joint research occurring at the intersection of Industrial Engineering and Computer Science is both necessary and synergistic in this project since the algorithmic aspects of promising and advanced computational approaches for technological realization are intertwined when research is performed in a large-scale systems context. Algorithms will be implemented in an object-oriented, event-driven, memory-resident, multi-threaded architecture for detailed study and empirical evaluation. One of the most important short-term customer service decisions is making accurate promises in response to requests from customers or business partners. Surprisingly, very little research has been done in this area. While a vast body of literature exists on scheduling to meet prescribed due dates, very little work addresses how due dates that are tight yet achievable may be assigned. Results of this research will increase the accuracy and speed with which these due date promises can be made, which in turn will have a very significant impact on revenues, operating expenses, and customer satisfaction. This research is directly applicable to manufacturers that increasingly are selling built-to-order products direct to customers via the Internet and to a future where collaborative commerce freely occurs among dynamically recombinant business partners.