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

这个可扩展的企业系统第二阶段资助的可扩展技术的调查，实时订单承诺离散的建设，以面对动态订单到达的环境。 本项目中开发的用于计算交货期的算法准确地考虑了资源和材料的当前时间阶段可用性、先前的订单承诺以及当前系统状态。 各种替代品（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合理的启发式技术，（2）可扩展的最佳算法，如最短路径，和（3）计算技术。 其中一个要检查的算法是基于一个新的，甚至是有争议的想法--为了保证，资源可用于处理操作的时间可以通过只考虑部分排序的任务计划来估计。 目前对这一原则的支持是基于实际经验，但几乎没有科学证据。 该项目的一个关键原则是结果具有高度可扩展性。 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是必要的和协同的，因为当研究在大规模系统环境中进行时，有前途的和先进的技术实现计算方法的算法方面是交织在一起的。 算法将在一个面向对象的，事件驱动的，内存驻留的，多线程的体系结构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作出准确的承诺，以响应客户或业务合作伙伴的请求。 令人惊讶的是，这方面的研究很少。 虽然大量的文献存在于调度，以满足规定的截止日期，很少有工作地址如何到期日期紧张，但可以实现分配。 这项研究的结果将提高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大的影响。 这项研究是直接适用于制造商，越来越多地通过互联网直接向客户销售定制产品，并在未来的合作商业自由地发生在动态重组的商业伙伴。

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.