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SIMULATION STUDY OF A DYNAMIC AGV-CONTAINER JOB DEPLOYMENT SCHEME

Automated Guided Vehicle (AGV) Container-Job deployment is essentially a vehicle dispatching problem. In this problem, the impact of vehicle dispatching polices on the ship makespan for discharging and/or loading operations is analyzed. In particular, here the storage location of each container is an input to the simulation model and is known. Thus, given a storage location for each container to be discharged from the ship and given the current location of each container to be loaded onto the ship, the problem is how to dispatch vehicles to containers so as to minimize the makespan of the ship so as to increase the throughput. The makespan of the ship refers to the time a ship spends at the port for loading and unloading operations. Automated Guided Vehicle System (AGVS) forms a very important part of automated material handling and its performance affects the efficiency of the entire system. Deadlock formation is a serious problem as it stalls the AGVS. Thus, the objective of this project is to develop an efficient deployment algorithm scheme, which will incorporate and integrate with the deadlock prediction and avoidance algorithm done in previous study [1]. The prediction & avoidance algorithm aims to predict and avoid cyclic deadlock. This project will compare the performance of current deployment scheme used by PSA with the new proposed deployment scheme, both with deadlock prediction & avoidance algorithm. The current deployment scheme, namely PMDS makes use of a greedy heuristics which dispatches the available vehicle that will reach the quay with the minimum amount of time the vehicle has to spend waiting for the crane to discharge or load the container from or onto the ship. The new deployment scheme