In a semiconductor fabrication facility, complex product flows involving hundreds of machines, many reentrant loops and various uncertainties (e.g., diverse equipment characteristics) pose a challenge in generating a production schedule that will ensure meeting of the product targets without excessive cycle time. In order to overcome these difficulties, a model predictive control (MPC) approach based on an aggregated flow model is developed. The MPC method is used to determine aggregated machine utilization schedules for each machine group at every shift. This optimization-based MPC allows the scheduler to simultaneously solve the constraint-aware production optimization and in-process inventory control problems at each scheduling instance. The performance of the proposed method is evaluated on a modified Intel mini fab case considering multiple products and changes in demands for each product.
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机译：在半导体制造设施中，涉及数百台机器的复杂产品流量，许多重圈循环和各种不确定性（例如，不同的设备特征）在产生生产计划时构成挑战，这将确保在没有过度循环时间的情况下实现产品目标的会议。为了克服这些困难，开发了一种基于聚合流模型的模型预测控制（MPC）方法。 MPC方法用于确定每个机器组的聚合机器利用时间表。基于优化的MPC允许调度程序在每个调度实例中同时解决约束感知的生产优化和在过程中的库存控制问题。在考虑多种产品的改进的Intel Mini Fab案例上对所提出的方法的性能进行评估，以及每个产品的需求变化。
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