Dynamic scheduling on multi-objective flexible Job Shop

To solve the flexible Job Shop multi-objective dynamic scheduling problems, the mathematical model of multi-objective flexible workshop dynamic scheduling was established according to the objective function of minimizing the tardiness penalty and manufacturing time. A flexible multi-objective dynamic scheduling algorithm was proposed based on adaptive genetic algorithm aiming at the above model. The hybrid and cycle-driven rescheduling strategies were employed, and a chromosome encoding method based on the integration of sequence and processing machines was advanced. As a result, the algorithm could not only optimize the processing route and manufacturing sequence but also realize real-time dynamic scheduling required by equipment failure, temporary changes of manufacturing task, and periodic rescheduling factors. Performance of the proposed model and algorithm was evaluated through simulations, and the results demonstrated the feasibility and efficiency of the proposed model and algorithm. The influencing event factors and rescheduling cycle which affected the performance of dynamic scheduling were analyzed, and the disturbance factors, rescheduling cycle and their relationships with performances of dynamic scheduling were obtained, which could guide the manufacturing practice effectively.