Integration of process planning and scheduling using a hybrid GA/PSO algorithm

Abstract

Process planning and scheduling are two of the most important activities in the manufacturing system. Integration of process planning and scheduling (IPPS) is treated as a crucial technology to realize computer-integrated manufacturing system (CIMS). This paper proposes a new IPPS method which contains two phases: static phase and dynamic phase. In the static phase, the process network of each job is generated according to the static shop floor status, and in the dynamic phase, the process plan of each job and the scheduling plan are generated according to the current shop floor status. With consideration of the optimization criteria of both process planning and scheduling, a hybrid algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO) is presented for optimizing the IPPS problem. Furthermore, a new reactive scheduling method, in which the flexibility of the process plan is taken into account, is presented to improve the flexibility of rescheduling. Finally, the proposed IPPS method is compared with the three existing IPPS methods, which are nonlinear process planning (NLPP), closed-loop process planning (CLPP) and distributed process planning (DPP). Meanwhile, the reactive scheduling method is also compared with the right-shift rescheduling method and the route changing rescheduling strategy. The results show the effectiveness of the proposed IPPS method and the reactive scheduling method.