Summary
This paper considers the allocation of a fixed number of berths to a number of ships arriving at the port within the planning horizon for container handling by determining the berthing time and location, in terms of berth index, for each ship. The solution to this berth allocation problem (BAP) involves the optimization of complete schedules with minimum service time and delay in the departure of ships, subject to a number of temporal and spatial constraints. To solve such a multi-objective and multi-modal combinatorial optimization problem, this paper presents a multi-objective multi-colony ant algorithm (MOMCAA) which uses an island model with heterogeneous colonies. Each colony may be different from the other colonies in terms of the combination of pheromone matrix and visibility heuristic used. In contrast to conventional ant colony optimization (ACO) algorithms where each ant in the colony searches for a single solution, the MOMCAA uses an ant group to search for each candidate solution. Each ant in the group is responsible for the schedule of a particular berth in the solution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Imai, X. Sun, E. Nishimura, and S. Papadimitriou, Berth allocation in a container port: using a continuous location space approach, Transportation Research Part B: Methodological, 39(3), 199–221, 2005.
A. Imai, E. Nishimura, and S. Papadimitriou, The dynamic berth allocation problem for a container port, Transportation Research Part B: Methodological, 35(4), 401–417, 2001.
A. Imai, E. Nishimura, and S. Papadimitriou, Berth allocation with service priority, Transportation Research Part B: Methodological, 37(5), 437–457, 2003.
A. Imai, E. Nishimura, M. Hattori, and S. Papadimitriou, Berth allocation at indented berths for mega-containerships, European Journal of Operational Research, 179(2), 579–593, 2007.
Y. Guan, W.-Q. Xiao, R. K. Cheung, and C.-L. Li, A multiprocessor task scheduling model for berth allocation: heuristic and worst case analysis, Operations Research Letters, 30, 343–350, 2002.
K. H. Kim and K. C. Moon, Berth scheduling by simulated annealing, Transportation Research Part B: Methodological, 37(6), 541–560, 2003.
Y.-M. Park and K. H. Kim, A scheduling method for berth and quay cranes, OR Spectrum, 25, 1–23, 2003.
C.-L. Li, X. Cai, and C.-Y. Lee, Scheduling with multiple-job-on-one-processor pattern, IIE Transactions, 30, 433–445, 1998.
A. Lim, The berth planning problem, Operations Research Letters, 22(2–3), 105–110, 1998.
K. K. Lai and K. Shih, A study of container berth allocation, Journal of Advanced Transportation, 26, 45–60, 1992.
A. Imai, K. Nagaiwa, and W. T. Chan, Efficient planning of berth allocation for container terminals in Asia, Journal of Advanced Transportation, 31, 75–94, 1997.
E. Nishimura, A. Imai, and S. Papadimitriou, Berth allocation planning in the public berth system by genetic algorithms, European Journal of Operational Research, 131(2), 282–292, 2001.
M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 26(1), 1996.
M. Dorigo and L. M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, 1, 53–66, 1997.
C. M. Fonseca, Multiobjective genetic algorithms with application to control engineering problems, Dept. Automatic Control and Systems Eng., University of Sheffield, Sheffield, UK, Ph.D. Thesis, 1995.
M. Middendorf, F. Reischle, and H. Schmeck, Information exchange in multi colony ant algorithms, in Proceedings of the Workshop on Bio-Inspired Solutions to Parallel Processing Problems, Cancun, Mexico, Springer Lecture Notes in Computer Science, vol. 1800, pp. 645–652, 2000.
L. M. Gambardella and M. Dorigo, Ant-Q: a reinforcement learning approach to the traveling salesman problem, in Proceedings of the 12th International Conference on Machine Learning, pp. 252–260, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cheong, C.Y., Tan, K.C. (2008). A Multi-Objective Multi-Colony Ant Algorithm for Solving the Berth Allocation Problem. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-78297-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78296-4
Online ISBN: 978-3-540-78297-1
eBook Packages: EngineeringEngineering (R0)