A PSO-based multi-objective optimization approach to the integration of process planning and scheduling

This paper has presented a particle swarm optimization (PSO) based approach to handle a multi-objective integrated process planning and scheduling problem. The aim is to find a set of high-quality trade-off solutions. This is a combinatorial optimization problem with substantially large solution space, suggesting that it is highly difficult to find the best solutions with the exact search method. To account for it, a PSO-based algorithm is proposed by fully utilizing the capability of the exploration search and fast convergence. To fit the continuous PSO in the discrete modeled problem, a novel solution representation is introduced in the algorithm. Moreover, to improve the solution quality, a local search algorithm is used to perform on the stored elite solutions, which would facilitate the exploitation search in the regions with promising solutions. The numerical experiments have been performed to demonstrate the effectiveness of the proposed algorithm.