• 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.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1246-1250
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• 2011

This paper considers the container storage problem determining storage position of inbound containers in a container yard at a rail freight terminal. The objective of the problem is to minimize the total number of rehandles when storing/retrieving containers onto/from stacks in the yard. Rehandle implies the temporary removal from and placement back onto the stack to retrieval a target container. In order to solve the problem, we formulate the problem as a binary integer program.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.11a
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• pp.71-79
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• 2006

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.753-770
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• 1996

In order to reduce the number of rehandles during the loading operation of export containers in port container terminals, the storage location of each arriving container should be determined considering of its weight. We formulate the problem by a dynamic programming model to get the optimal storage location. And a heuristic rule is suggested in order to overcome computational difficulties of the optimization model. The performance of the rule is evaluated by comparing it with the result of DP model.

• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.717-723
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• 2018

Thermal analysis of accident conditions is an important problem during safety assessment of the dry spent nuclear fuel storage facilities. Thermal aspects of accident conditions with channel blockage of ventilated storage containers are considered in this article. Analysis of flow structure inside ventilated containers is carried out by numerical simulation. The main mechanisms of heat and mass transfer, which take part in spent nuclear fuel cooling, were detected. Classification of accidents on the basis of their influence on the maximum temperatures inside storage casks is proposed.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.299-305
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• 2020

We have developed a scheduler that additionally consider network performance by extending the Kubernetes developed to manage lots of containers in cloud computing nodes. The network delay adapt characteristics of the compute nodes were learned during server operation and the learned results were utilized to develop placement algorithm by considering the existing measurement units, CPU, memory, and volume together, and it was confirmed that the low delay network service was provided through placement algorithm.

• The monthly packaging world
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• s.148
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• pp.122-147
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• 2005

• The monthly packaging world
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• s.147
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• pp.56-59
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• 2005

• The monthly packaging world
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• s.147
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• pp.122-141
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• 2005

• The monthly packaging world
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• s.197
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• pp.94-103
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• 2009