AN EFFECTIVE GENETIC ALGORITHM FOR FLOW SHOP SCHEDULING PROBLEMS TO MINIMIZE MAKESPAN
Keywords:Flow shop scheduling, Makespan, Genetic Algorithm, Matlab.
In this paper, the flowshop scheduling problem with the objective of minimizing the makespan has important applications in an exceedingly type of industrial systems. The main concern of flow shop scheduling is to get the most effective sequence, that minimizes the makespan, time of flow, time of idle, delay, etc. the objective of minimizing makespan is planned for finding the flowshop scheduling problem with Effective Genetic algorithm (EGA). EGA could be an easy and efficient algorithm that is employed to resolve for each single and multi-objective problem in flow shop environment. This algorithm can works simply for our real life applications. The planned algorithm is tested with well-known problems in literature. EGA’s resolution performance has been compared with the present results reported by researchers. The obtained results show that the planned EGA performs higher than NPFS-ACO algorithms in finding the flowshop scheduling problem with the makespan criterion as average percentage improvement of 1.42%. This improvement ends up in two completely different meta-heuristic algorithms for finding flow shop planning problems specifically real coded Genetic algorithm (RCGA) and EGA. However EGA is performed well once comparing with RCGA.