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

该可扩展企业系统第二阶段资助了一项可扩展的实时订单技术的研究，该技术有望在面对动态订单到达的离散的按订单构建环境中实现。该项目中用于计算到期日期的算法将准确地考虑当前时间阶段的资源和材料的可用性、先前的订单承诺以及当前的系统状态。各种替代方案（资源路径或材料来源）的存在创造了组合的复杂性。为了提高性能，有前途的算法将整合(1)通过研究开发的理论上健全的启发式技术，(2)可扩展的最优算法，如最短路径，以及(3)计算技术。要检查的启发式方法之一是基于一个新颖的，甚至是有争议的想法——为了保证，可以通过仅考虑部分有序的任务计划来足够准确地估计可用于处理操作的资源的时间。目前对这一原则的支持是基于实践经验，而缺乏科学证据。这个项目的一个关键原则是结果是高度可伸缩的。因此，在这个项目中，工业工程和计算机科学交叉领域的联合研究既是必要的，也是协同的，因为在大规模系统环境中进行研究时，有前途的和先进的技术实现计算方法的算法方面是相互交织的。算法将在面向对象、事件驱动、内存驻留、多线程架构中实现，以进行详细的研究和实证评估。最重要的短期客户服务决策之一是根据客户或业务伙伴的要求做出准确的承诺。令人惊讶的是，这方面的研究很少。虽然有大量的文献介绍如何按时完成规定的截止日期，但很少有研究涉及如何分配紧迫但可以实现的截止日期。这项研究的结果将提高这些到期日期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大的影响。这项研究直接适用于越来越多的制造商通过互联网直接向客户销售按订单生产的产品，也适用于未来在动态重组的业务伙伴之间自由地进行协作商业的未来。

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.