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2022
Conference Paper
Titel
A Digital Twin-based Approach Performing Integrated Process Planning and Scheduling for Service-based Production
Abstract
Nowadays, the automation industry is undergoing many changes, manufactures must react to fast changing market demands and more individual customer requirements. Recept-based, Service-Oriented Architectures enable the efficient adaption of production processes to new operating conditions. However, to ensure production performance, service-oriented production should also be complemented by adequate scheduling approaches to guarantee critical performance factors. We present a Digital Twin-based approach that performs integrated process planning and scheduling for service-based production. For our approach, we have identified a common set of input data required for integrated process planning and scheduling. We use Deep-Q-Network, which is a deep Reinforcement Learning method, to derive near optimal schedules for production conditions described in Digital Twins. If an unforseen event happens during the production, our approach is able to adapt current schedules to the changed operating conditions. The case study shows that our approach is able to derive near optimal schedules for customized products and adapt its currents schedules for new orders with different production goals.