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 todays 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.
Holonic Manufacturing System Particle swarm optimization Process planning Scheduling
FU-QING ZHAO QIU-YU ZHANG YA-HONG YANG
School of Computer and Communication Engineering, Lanzhou University of Technolog, Lanzhou 730050, C School of Civil Engineering, Lanzhou University of Technolog, Lanzhou 730050, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
396-401
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）