This paper compares and evaluates the performance of five different conventional job selection dis-patching rules for scheduling of multi-load automatic guided vehicles (AGVs) serving for material handling operations in variable sized flexible manufacturing system (FMS) layout. Four sizes of FMS layout are considered for the performance evaluation of the five types of conventional job se-lection dispatching rules. The FMS layouts under consideration are served by the two multi-load AGVs. The multi-load AGVs cruises under machine initiated the nearest vehicle (NV) dispatching rule for the material handling activities at all work centers (WCs) for all four sizes of FMS layout. Four sizes of FMS layout produce five different types of parts and consist of three, six, nine and twelve work centers and loading-unloading centers, respectively. In the simulation test, it is found that the identical destination first (IDF) job selection rule having selection criterion based on the destination similarity of two picked up jobs outperforms all other job selection dispatching rules for an overall production rate of the FMS (parts/hr) in all four FMS layouts.

The application of material handling robots (MHRs) has been commonly observed in flexible manufacturing systems (FMS) for efficient material handling activities. In order to gain maximum throughput, minimum tardiness from the minimum investment of funds for the material handling activities, it is important to determine the optimum numbers of MHRs required for efficient production of jobs in the FMS. In the present work, the requirement of MHRs is optimized for different FMS layouts by using a heuristic procedure. Initially, a mathematical model is proposed to identify the MHRs requirement to perform the material handling activities in the FMS, later on, the model is optimized by simulating a novel heuristic procedure to find the required optimum number of MHRs in the FMS. The proposed methodology is found to be generic enough and can also be applied in various industries employing the MHRs.

The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the effi-cient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced workload for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.