Application of Genetic Algorithm on Spare Part Automotive Body Scheduling - Rendy -; Anastasia Lidya Maukar; Arthur Silitonga (International Journal of Industrial Research and Applied Engineering, 2016)
Job shop scheduling is one of the complex problems in the manufacturing industry, such as an automotive body manufacturer. This manufacturing company, located in Cikarang, Indonesia, deals with huge customer demand that leads to difficulties in production scheduling. This will cause a delay in some jobs and product deliveries. The current system is using the semi-active scheduling approach and requires 637 minutes for performing 6 jobs with 5 machines. The genetic algorithm (GA) model is proposed as an alternative solution to solve this problem. The GA parameter is set as follow: The population size expected is 30 with maximum generation can be produced in amount of 50. The crossover rate, mutation, and preservation are set to 0.3, 0.1, and 0.1, respectively. After 50 generations are obtained, the optimum solution is shown in generation 6 with a makespan of 597 minutes. Thus, the genetic algorithm model is effectively reducing the makespan of the job-shop scheduling problem by 10% compared to the current method applied at the company.