Data-Driven Scheduling of Cellular Manufacturing Systems Using Process Mining with Petri Nets

Petri net is a mathematical model for representing parallel, asynchronous, and distributed systems. Petri nets can model parallel and synchronous activities in manufacturing systems at various levels of abstraction. In this study, we propose data-driven modeling and scheduling for cellular manufacturing systems using process mining with Petri nets. In the proposed method, the event log data is extracted from a virtual plant and then the Petri net model considering the movement of products and operators is developed by using the process mining technique with the Petri net model. We also derived an approximate solution for the derived Petri net model from the event log using a local search method using a Petri net simulator. The analysis and modification of the model are conducted in the proposed method. Near-optimal schedules are derived using Petri net simulations. The validity of the proposed model is evaluated.