Hybrid approach to production scheduling using genetic algorithm and simulation

In the production scheduling problem, due to various kinds of uncertain factors such as queuing, breakdowns and repairing time of machines, the optimal solution considering the stochastic behaviour of a real operation cannot be easily solved. To solve the problem, we present a hybrid approach with a genetic algorithm (GA) and a simulation. The GA is used for optimization of schedules, and the simulation is used to minimize the maximum completion time for the last job with fixed schedules from the GA model. We obtain more realistic production schedules with an optimal completion time reflecting stochastic characteristics by performing the iterative hybrid GA - simulation procedure. It has been shown that the hybrid approach is powerful for complex production scheduling.