• Journal of Navigation and Port Research
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• v.34 no.6
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• pp.503-509
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• 2010

Remarshaling is referred to a preparatory task of rearranging containers piled up in a stacking yard to improve the efficiency of loading. Selective remarshaling is required because the time for remarshaling known as large time-consuming task is not enough to remarshal all containers. In this research, we compare two previous researches in more objectively way: heuristic and genetic algorithm based approaches. In addition, we prove the effect of iterative reselection method on dwindling the gap between plan and execution due to the uncertainty of crane operation during execution. Simulation experiments under realistic uncertainty model show that heuristic approaches which have few computational complexity can cope with the uncertainty well when reselection interval is short, but genetic algorithm based approach has advantages when reselection interval that can ensure appropriate number of evolutions is given because of computational complexity for search.

• Journal of Navigation and Port Research
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• v.40 no.3
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• pp.113-120
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• 2016

If there are containers stacked upon the container to be fetched out of a container yard to vessel, rehandling which moves those containers to other places temporarily is needed. In order to avoid such rehandling, remarshalling which rearranges containers should be done before the vessel arrives. The remarshalling plan is commonly generated within a bay. It happens, however, that the generation of the intra-bay remarshalling plan within the permitted time is not possible because of bad stacking conditions. This paper presents the remarshalling algorithm which uses the empty slots of the neighboring bay as a temporary storage space. Simulation experiments have shown that the presented algorithm can generate the remarshalling plan within the permitted time under any staking conditions.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.25-43
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• 2005

In modern automated container terminals, automated guided vehicle (AGV) systems are considered a viable option for the horizontal tansportation of containers between the stacking yard and the quayside cranes. AGVs in a container terminal move rather freely and do not follow fixed guide paths. For an efficient operation of such AGVs, however, a sophisticated traffic management system is required. Although the flexible routing scheme allows us to find the shortest possible routes for each of the AGVs, it may incur many coincidental encounters and path intersections of the AGVs, leading to collisions or deadlocks. However, the computational cost of perfect prediction and avoidance of deadlocks is prohibitively expensive for a real time application. In this paper, we propose a traffic control method that predicts and avoids some simple, but at the same time the most frequently occurring, cases of deadlocks between two AGVs. More complicated deadlock situations are not predicted ahead of time but detected and resolved after they occur. Our method is computationally cheap and readily applicable to real time applications. The efficiency and effectiveness of our proposed methods have been validated by simulation.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.59-79
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• 2005

Export containers in a container terminal are usually classified into a few weight groups and those belonging to the same group are placed together on a same stack. The reason for this stacking by weight groups is that it becomes easy to have the heavier containers be loaded onto a ship before the lighter ones, which is important for the balancing of the ship. However, since the weight information available at the time of container arrival is only an estimate, those belonging to different weight groups are often stored together on a same stack. This becomes the cause of extra moves, or rehandlings, of containers at the time of loading to fetch out the heavier containers placed under the lighter ones. In this paper, we use machine learning techniques to derive a classifier that can classify the containers into the weight groups with improved accuracy. We also show that a more useful classifier can be derived by applying a cost-sensitive learning technique, for which we introduce a scheme of searching for a good cost matrix. Simulation experiments have shown that our proposed method can reduce about 5$\sim$7% of rehandlings when compared to the traditional weight grouping method.