A genetic algorithm based approach for integration of process planning and production scheduling

Process planning and production scheduling play important roles in manufacturing systems. Their roles are to ensure the availability of manufacturing resources needed to accomplish production tasks result-from a demand forecast. In this paper, a fuzzy inference system(FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system are proposed. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values are 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 genetic algorithms 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.