Abstract:
PT. X as one of manufacturing company that produces electrical panels, has deal with huge demand from customer that leads to the unbalance of job and machine. This will affect to the delay in some jobs and delivery of the product because the jobs are incomplete. The current system not using scheduling approach and requires 248 minutes to do 9 jobs with 5 machines. Then genetic algorithm used manual calculation compared to Campbell, Dudek, and Smith (CDS) Method. Knowing that GA is a more effective method next is calculation using software. The genetic algorithm model is proposed using numerical computation software after validation and verification are done. Genetic Algorithm (GA) is done through several steps, including initialization, determining objective function, selection (roulette-wheel), crossover (one-point), mutation (order changing), and breeder GA. The current system and proposed model are being compared to show the differences. After 50 generations are obtained, the optimum solution is shown with makespan 237 minutes. Thus, genetic algorithm model is effectively reducing the makespan of the flow shop scheduling problem by 4.43%.
