FUZZY GENETIC ALGORITHM MODEL FOR OPTIMIZATION OF AUTOMATED GUIDED VEHICLE SCHEDULING

The current trend in manufacturing technology is considered by two main items, automation and flexibility. Flexible manufacturing system (FMS) is one of the most identified systems that include both automation and flexibility criteria. An FMS comprises three principle elements: computer controlled machine tools; an automated material handling system and a computer control system. One of the automated materials handling equipment in FMS is automated guided vehicles (AGVs) those are one of the material handling equipments in FMS. Integrated scheduling of AGVs and machine machines is an essential factor contributing to the efficiency of the manufacturing system in FMS environment. Before genetic algorithm (GA) considered as a heuristic method to solve AGV scheduling problem. GA maybe notable to achieve the global optimum and sticks in local optima and premature convergence occur. Fuzzy logic controller (FLC) is proposed to control the behavior of GA during solving the scheduling problem of AGVs. This paper presents a job-based GA that based on job sequencing during the optimization and FLC control crossover and mutation rate in simultaneous machine and AGV scheduling problem.