Permutation based Genetic Algorithm with Event-Scheduling/Time-Advance Algorithm as Decoder for a Flexible Job-shop Scheduling Problem
Today, numerous research support the growing scheduling problems that exist globally in competitive businesses. Scheduling needs to become efficient in order to remain relevant against competitors. Simulations need to provide results in short periods of time so that adjustments can be made and unnecessary costs can be avoided. Scheduling problems have become larger in size and greater in complexity given the rising product variations and increase in variety for manufacturing equipment. Hence, there is a practical need for genetic algorithms solving scheduling problems to be fast and versatile. This thesis introduces an event-scheduling/time-advance algorithm for the decoder to reduce the load on the genetic algorithm with a smaller global search space. Consequently, convergence can be reached sooner and larger problems can be tackled easily. The structure of this heuristic algorithm allows metrics to be easily implemented in order to give the user performance measures on the scheduling problem.