A genetic algorithm for integration of process planning and scheduling in a job shop

In recent manufacturing systems, most jobs may have several process plans, such as alternative sequence of operations, alternative machine, etc. A few researches have addressed the necessity for the integration of process planning and scheduling function for efficient use of manufacturing resources. But the integration problem is very difficult and complex. Many prior researches considered them separately or sequentially. It introduces overlapping or partial duplications in solution efforts. In this paper, integration problem of jobs with multiple process plans in a job shop environment is addressed. In order to achieve an efficient integration between process planning and scheduling by taking advantage of the flexibility that alternative process plans offer, we designed GA (Genetic Algorithm)-based scheduling method. The performance of proposed GA is evaluated through comparing integrated scheduling with separated scheduling in real world company with alternative machines and sequences of operations. Also, a couple of benchmark problems are used to evaluate performance. The integrated scheduling method in this research can be effectively applied to the real case.