Integration of process planning and scheduling using simulation based genetic algorithms

Process planning and scheduling are traditionally regarded as separate tasks performed sequentially; but, if the two tasks are performed concurrently, greater performance and higher productivity of a manufacturing system can be achieved. Although several workers have addressed the process plan selection problem in recent years, their main approaches are to select process plans from plan alternatives by taking into account the similarities among process plans of the parts. In this paper, we propose a new approach to the integration of process planning and scheduling using simulation based genetic algorithms. A simulation module computes performance measures based on process plan combinations instead of process plan alternatives and those measures are fed into a genetic algorithm in order to improve the solution quality until the scheduling objectives are satisfied. Computational experiments show that the proposed method reduces significantly scheduling objectives such as makespan and lateness.