Improved genetic algorithm with external archive maintenance for multi-objective integrated process planning and scheduling

Process planning and scheduling are two important functions in modern manufacturing system. Considering their complementarity, integrating process planning and scheduling more tightly could improve the performance and productivity of the whole manufacturing system. Meanwhile, multi-objective optimization problem is widespread existing in practice. The decision maker always needs to make a trade-off between two or more objectives while determining a final schedule. In this paper, an improved genetic algorithm (IGA) with external archive maintenance is proposed to optimize the multi-objective integrated process planning and scheduling (IPPS) problem. IGA is utilized to search for the Pareto optimal solutions, while the external archive is used to store and maintain the generated non-dominated solutions during the optimization procedure. Three different scale instances have been employed to test the performance of the proposed algorithm. The experiment results show that the proposed algorithm has achieved satisfactory improvement.