An improved particle swarm optimization (PSO) algorithm and fuzzy inference systems based approach to process planning and production scheduling integration in holonic manufacturing system (HMS)

New paradigms for manufacturing system control are required that provide manufacturers with the adaptability and responsiveness required to compete in today's market. In this paper, an integrated process planning and scheduling system, which is applicable to the holonic manufacturing system is presented. Basic architecture of the target holonic manufacturing system is discussed from the viewpoint of the process planning and the scheduling systems. Process planning are proposed to select suitable machining sequences of machining features and suitable sequences of machining equipment, taking into consideration of future schedules of machining equipment. A fuzzy inference system(FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS is presented. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, an improved particle swarm optimization(PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.