Ant Colony Optimization for Solving Container Stacking Problems

Phatchara Sriphrabu; Kanchana Sethanan; Somnuk Theerakulpisut

doi:10.4028/www.scientific.net/AMR.931-932.1689

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 931-932Ant Colony Optimization for Solving Container...

Ant Colony Optimization for Solving Container Stacking Problems

Abstract:

This paper reports the results of a study to use the ant colony optimization (ACO) in solving an export container stacking and storage problem of a container terminal port. Three different methods of solutions based on different initial solution techniques and the current method used by the port were used to test forty sample problems which were classified into four problem types representing different patterns of container arrivals. The results of this study revealed that the three methods based on the ACO could be used to improve the total time taken for stacking and storage of the export containers for all problem types. Among the methods investigated, the heuristic-based ant colony optimization (BACO) method proved to be the most efficient for all problem types, with the relative improvements over the current method ranging from 25.4-38.7%.

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Periodical:

Advanced Materials Research (Volumes 931-932)

Pages:

1689-1695

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.931-932.1689

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Online since:

May 2014

Authors:

Phatchara Sriphrabu, Kanchana Sethanan*, Somnuk Theerakulpisut

Keywords:

Ant Colony Optimization, Container Terminal Port, Container Yard, Stacking, Storage

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References

[1] L. Chen, Z. Lu, The storage location assignment problem for outbound containers in a maritime terminal, Int. J. Prod. Econ. 135 (2012) 73-80.

DOI: 10.1016/j.ijpe.2010.09.019

Google Scholar

[2] KH. Kim, HB. Kim, Segregating space allocation models for container inventories in port container terminals, Int. J. Prod. Econ. 59 (1999) 415-423.

DOI: 10.1016/s0925-5273(98)00028-0

Google Scholar

[3] KH. Kim, Y-M. Park, M-J. Jin, An optimal layout of container yards, OR Spectrum 30 (2008) 675–695.

DOI: 10.1007/s00291-007-0111-6

Google Scholar

[4] KH. Kim, GP. Hong, A heuristic rule for relocating blocks. Comput, Oper. Res. 33 (2006) 940-954.

Google Scholar

[5] D. Ambrosino, A. Sciomachen, E, Tanfani, Stowing a containership: the master bay plan problem. Transp. Res. Pt. A-Policy Pract. 38 (2004) 81-99.

DOI: 10.1016/j.tra.2003.09.002

Google Scholar

[6] P. Preston, E. Kozan, An approach to determine storage locations of containers at seaport terminals. Comput. Oper. Res. 28 (2001) 983-995.

DOI: 10.1016/s0305-0548(00)00020-4

Google Scholar

[7] MA. Salido, O. Sapena, M. Rodriguez, F. Barber, A planning tool for minimizing reshuffles in container terminals. In: Proceedings of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2009), New Jersey, pp.567-571.

DOI: 10.1109/ictai.2009.53

Google Scholar

[8] M. Dorigo, V. Maniezzo, A. Colorni, The ant system: Optimization by a colony of cooperating agents. IEEE Trans. On System, 26 (1996) 1-13.

DOI: 10.1109/3477.484436

Google Scholar