Dynamic classified JSP scheduling based on petri net and GASA

A new classified scheduling method based on the controlled Petri net and GASA was proposed to the job-shop scheduling problem (JSP) with multiple disturbances constrained by machines, workers. Firstly, a Petri net with controller is modeled, it not only has the modeling capability of a traditional Petri net, but also it can depict system characteristics, such as equipment maintenance, different types of priorities, and so on; and then the hybrid genetic algorithm and simulated annealing algorithm (GASA) was applied based on the controlled Petri net model. Function objective of the proposed method was to minimize the completion time, and scheduling was classified based on machine repairing time, and worker leaving time, and task of order canceling. In order to avoid unsteady state of processing due to regulate in large scale, and the job shop production ability can be maintained farthest, it was decided whether it was rescheduled again based on remainder task after disturbance resuming. Simulation results based on some job shop scheduling show that the GASA is efficient.