Application of Fuzzy Inference Systems and Genetic Algorithms in Integrated Process Planning and Scheduling

This paper proposes a fuzzy inference system in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The MTTF 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. An example of simulation problem of a 4 jobs 3 machines environment is presented. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling