A simulation approach to the process planning problem using a modified particle swarm optimization

Abstract

Due to the complexity and variety of practical manufacturing conditions, computer-aided process planning (CAPP) systems have become increasingly important in the modern production system. In CAPP, the process planning (PP) problem involves two tasks: operation determining and operation sequencing. To optimize the process plans generated from complex parts, the traditional particle swarm optimization (PSO) algorithm is modified. Efficient encoding and decoding population initialization methods have been developed to adapt the PP problem for the PSO approach. In addition, to avoid the proposed approach becoming trapped in local convergences and achieving local optimal solutions, parameters are set to control the iterations. Several extended operators for the different parts of the particles have been incorporated into the traditional PSO. Simulation experiments have been run to evaluate and verify the effectiveness of the modified PSO approach. The simulation results indicate that the PP problem can be more effectively solved by the proposed PSO approach than other approaches.