GOALI: Process Control Approaches to Supply Chain Management in Semiconductor Manufacturing

充满活力的制造业对美国的经济健康至关重要，有效的供应链管理在这方面发挥着关键作用。这笔赠款提供资金，用于探索模型预测控制(MPC)--一种源于流程工业的先进控制范例--如何作为一种新方法，用于解决与半导体制造相关的供应链管理问题的战术决策。该项目涉及亚利桑那州立大学化学工程、数学和计算机科学的研究人员与英特尔公司的技术负责人的独特合作。主要的研究主题是针对一类与半导体制造相关的新型供应链问题的模型预测控制算法的公式和初始概念验证，这些供应链问题超越了化学过程应用和传统的MPC。为此，将研究如何使用最先进的优化技术来有效地解决与模型预测控制相关的优化问题，以及开发一种能够满足这类供应链问题的独特计算需求的软件体系结构。预计作为本研究的一部分开发的基于模型预测控制的公式最终将作为分层的、企业范围的计划工具的组成部分，这些工具基于实时数据，支持不同级别的信息共享和集中化，并采用组合的反馈-前馈控制行动。这项研究的更广泛影响包括努力发展一套不仅对半导体制造有意义的理论和技术，而且对广泛的对国民经济重要的离散部件制造业有意义。由于这笔赠款，私人投资机构正在努力让少数族裔和本科生参与综合研究和教育活动，并将研究成果传播给广大工程界，这将进一步刺激他们的努力。

A vibrant manufacturing sector is vital to the economic health of the United States, and efficient management of supply chains plays a critical role in this regard. This grant provides funding to explore how Model Predictive Control (MPC), an advanced control paradigm originating from the process industries, can be utilized as a novel approach for tactical decision-making in supply chain management problems associated with semiconductor manufacturing. The project involves the unique collaboration of investigators from chemical engineering, mathematics, and computer science at Arizona State University with a technical leader from Intel Corporation. The principal topic of research is the formulation and initial proof-of-concept of Model Predictive Control algorithms for a novel class of supply chain problems associated with semiconductor manufacturing that extend beyond chemical process applications and traditional MPC. To this end, the use of state-of-the-art optimization techniques that efficiently solve the optimization problems associated with Model Predictive Control, and the development of a software architecture that can satisfy the unique computational needs of this class of supply chain problems, will be examined.It is expected that the Model Predictive Control-based formulations developed as part of this research will ultimately serve as integral components in hierarchical, enterprise-wide planning tools that function on real-time data, support varying levels of information sharing and centralization, and employ combined feedback-feedforward control action. Broader impacts of this research include efforts towards developing a body of theory and technology meaningful not only to semiconductor manufacturing, but to a wide range of discrete-part manufacturing industries of importance to the national economy. Ongoing efforts by the PIs to involve minority and undergraduate students in integrative research and educational activities, as well as the dissemination of research outcomes to the engineering community at large, will be further stimulated as a result of this grant.