Integration of process planning and production scheduling with particle swarm optimization (PSO) algorithm and fuzzy inference systems

Abstract

Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the 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.

A review on Integrated Process Planning and Scheduling

“…Guo et al (2009) developed the IPPS as a combinatorial optimisation model, and modified a Particle Swarm Optimisation (PSO) algorithm to solve IPPS. Yang et al (2005) and Zhao et al (2006aZhao et al ( , 2006b used a fuzzy inference system to choose alternative machines for IPPS of a job shop manufacturing system, and used the hybrid PSO algorithms to balance the load of each machine. Palmer (1996) proposed an SA-based approach to solve IPPS.…”