Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm

Abstract

The current trend in manufacturing technology is considered by two main items automation andflexibility. Flexible manufacturing system (FMS) is one of the most identified systems that include bothautomation and flexibility criteria. It comprises three principle elements: computer controlled machinetools, an automated material handling system and a computer control system. One of the automatedmaterials handling equipment in FMS is automated guided vehicles (AGVs). Integrated scheduling ofAGVs and machines is an essential factor contributing to the efficiency of the manufacturing system inFMS environment. Previously, genetic algorithm (GA) is considered as a heuristic method to solve AGVscheduling problem. GA may not be able to achieve the global optimum due to premature convergencebecause of control’s lack on its operators parameters. Fuzzy logic controller (FLC) is proposed tocontrol the behavior of GA during solving the scheduling problem of AGVs. This paper presents a job-based GA that is based on job sequencing. Through the optimization, the FLC is used to control the GAoperators (crossover and mutation rate) simultaneous to solve the AGV scheduling problem