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

一个充满活力的制造业部门对美国的经济健康至关重要，而供应链的有效管理在这方面发挥着关键作用。 该基金提供资金，以探索如何模型预测控制（MPC），一个先进的控制范例起源于过程工业，可以作为一种新的方法，在与半导体制造相关的供应链管理问题的战术决策。 该项目涉及来自亚利桑那州立大学的化学工程、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。 研究的主要课题是模型预测控制算法的制定和初步概念验证，用于与半导体制造相关的一类新的供应链问题，这些问题超出了化学过程应用和传统MPC。 为此，使用最先进的优化技术，有效地解决与模型预测控制相关的优化问题，并开发能够满足这类供应链问题的独特计算需求的软件架构，将被检查。预计模型预测控制-作为这项研究的一部分开发的基于公式的方法最终将作为分层的、企业范围的规划工具的组成部分，这些工具在实时数据上发挥作用，支持不同级别的信息共享和集中化，并采用组合的反馈-前馈控制动作。这项研究的更广泛的影响包括努力开发一套不仅对半导体制造业有意义的理论和技术，而且对国民经济重要的各种离散部件制造业也有意义。 PI正在努力让少数民族和本科生参与综合研究和教育活动，以及将研究成果传播到整个工程界，这将进一步刺激由于这笔赠款。

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.