Approach for Dynamic Job Shop Scheduling Based on GASA

A Petri net with controller is used to model discrete events in flexible job shop scheduling with results obtained based on genetic algorithm and simulated annealing algorithm (GASA), and the method is developed to address the dynamic scheduling problem with dual-resource constraints and multiple disturbances in manufacturing systems. The objective of scheduling problems is to minimize make-span, after using crossover, mutation, probabilistic updating strategy and re-assignment strategy and so on genetic and simulated operation, a best or second best scheduling plan can be found. Especially important, it is capable of generating alternative schedule after an uncertain disturbance takes place on a job shop. Simulation results based on some job shop scheduling show that the GASA is efficient and robust.