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CLC number: TP399; U294.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-10-29
Cited: 3
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Distributed framework for yard planning in container terminals
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Kap Hwan Kim, Xiao-hui Zhang. Distributed framework for yard planning in container terminals[J]. Journal of Zhejiang University Science A, 2010, 11(12): 992-997.
@article{title="Distributed framework for yard planning in container terminals",
author="Kap Hwan Kim, Xiao-hui Zhang",
journal="Journal of Zhejiang University Science A",
volume="11",
number="12",
pages="992-997",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1001527"
}
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%A Kap Hwan Kim
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%DOI 10.1631/jzus.A1001527
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T1 - Distributed framework for yard planning in container terminals
A1 - Kap Hwan Kim
A1 - Xiao-hui Zhang
J0 - Journal of Zhejiang University Science A
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Y1 - 2010
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1001527
Abstract: This study discusses a yard planning system, which considers various resources such as storage space, yard cranes, and traffic areas in container terminals. The system is based on the function for estimating resource requirements of yard plans. For a given yard plan, the proposed system allows planners to check the feasibility of the plan which requires a certain amount of workload of resources in related blocks during a planning horizon. The yard planning system in this study is aimed at balancing workloads among the blocks and providing the ability to modify current yard plans by detecting blocks and periods with overloaded workloads. The system implements its planning function in a distributed manner in which planners construct yard plans under their individual control and send and receive only limited necessary information for the negotiation.
[1]Bazzazi, M., Safaei, N., Javadian, N., 2009. A genetic algorithm to solve the storage space allocation problem in a container terminal. Computers & Industrial Engineering, 56(1):44-52.
[2]Han, Y., 2007. Efficient Yard Storage in Transshipment Container Hub Ports. PhD Thesis, National University of Singapore, Singapore.
[3]Kim, K.H., Park, K.T., 2003. A note on a dynamic space allocation method for outbound containers. European Journal of Operational Research, 148(1):92-101.
[4]Kozan, E., 2000. Optimising container transfers at multimodal terminals. Mathematical and Computer Modelling, 31(10-12):235-243.
[5]Lee, L.H., Chew, E.P., Tan, K.C., Han, Y., 2006. An optimization model for storage yard management in transshipment hubs. OR Spectrum, 28(4):539-561.
[6]Preston, P., Kozan, E., 2001. A tabu search technique applied to scheduling container transfers. Transportation Planning and Technology, 24(2):135-154.
[7]Won, S.H., 2009. Operational Planning in Container Terminals under Resource Restrictions. PhD Thesis, Pusan National University, Korea.
[8]Zhang, C., Liu, J., Wan, Y.W., Murty, K.G., Linn, R.J., 2003. Storage space allocation in container terminals. Transportation Research, 37B:883-903.
[9]Zhang, X.H., 2010. A Space Planning Method Considering Workloads in Container Terminals. MS Thesis, Pusan National University, Korea.
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