Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. FMS are well suited for simultaneous production of a wide variety of part types in low volumes. The FMS elements can operate in an asynchronous manner and the scheduling problems are more complex. The use of Automated Guided Vehicle is increasing day by day for the material transfer in production lines of modern manufacturing plants. The purpose is to enhance efficiency in material transfer and increase production. Though the hardware of AGV’s has made significant improvement in the field but the software control of the fleet still lacks in many applications. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). In this work, scheduling of job is done for a particular type of FMS environment by using an optimization technique called the genetic algorithm (AGA). A ‘C’ programming code was developed to find the optimal solution. When a chromosome is input, the GA works upon it and produces same no. of offsprings. The no. of iterations take place until the optimum solution is obtained. Here we have worked upon eight problems, with different no. of machines and no. of jobs. The input parameters used are Travel Time matrix and Processing Time matrix with the no. of machines and no. of jobs. The results obtained are very quite close to the results obtained by other techniques and by other scholars.
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