Abstract:
PT. XYZ as one of manufacturing company that produces spare part for the
automotive bodies, 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. In addition, there is no
integrated system for the production scheduling; the jobs orders are determined
based on fist come first serve rule. The current system is using semi-active
scheduling approach and requires 637 minutes to do 6 jobs with 5 machines. 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 using semi-active scheduling
approach and being compared to show the differences. After 50 generations are
obtained, the optimum solution is shown in generation 6 with makespan 597
minutes. Thus, genetic algorithm model is effectively reducing the makespan of
the job shop scheduling problem by 10%.
