Abstract
Seaside operations at container ports often suffer from uncertainty due to events such as the variation in arrival and/or processing time of vessels, weather conditions and others. Finding a robust plan which can accommodate this uncertainty is therefore desirable to port operators. This paper suggests ways to generate robust berth allocation plans in container terminals. The problem is first formulated as a mixed-integer programming model whose main objective is to minimize the total tardiness of vessel departure time. It is then solved exactly and approximately. Experimental results show that only small instances of the proposed model can be solved exactly. To handle large instances in reasonable times, the Genetic Algorithm (GA) is used. However, it does not guarantee optimality and often the approximate solutions returned are of low quality. A hybrid meta-heuristic which combines Branch-and-Cut (B&C) as implemented in CPLEX, with the GA as we implement it here, is therefore suggested. This hybrid method retains the accuracy of Branch-and-Cut and the efficiency of GA. Numerical results obtained with the three approaches on a representative set of instances of the problem are reported.
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
References
Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005). Elsevier
Cheong, C., Tan, K., Liu, D., Lin, C.: Multi-objective and prioritized berth allocation in container ports. Ann. Oper. Res. 180(1), 63–103 (2010). Springer
Fister, I., Fister, J., Yang, X., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013). Elsevier
Ganji, S., Babazadeh, A., Arabshahi, N.: Analysis of the continuous berth allocation problem in container ports using a genetic algorithm. Mar. Sci. Technol. 15(4), 408–416 (2010). Springer
Goh, K., Lim, A.: Combining various algorithms to solve the ship berthing problem. In: 12th IEEE International Conference on ICTAI, pp. 370–375. IEEE (2000)
Guan, Y., Cheung, R.: The berth allocation problem: models and solution methods. OR Spectrum 26(1), 75–92 (2004). Springer
Holland, J.: The Grid: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)
Imai, A., Sun, X., Nishimura, E., Papadimitriou, S.: Berth allocation in a container port: using a continuous location space approach. Transp. Res. Part B Methodol. 39(3), 199–221 (2005). Elsevier
Lee, D., Chen, J., Cao, J.: The continuous berth allocation problem: a greedy randomized adaptive search solution. Transp. Res. Part E Logist. Transport. Rev. 46(6), 1017–1029 (2010). Elsevier
Lee, Y., Chen, C.: An optimization heuristic for the berth scheduling problem. Eur. J. Oper. Res. 196(2), 500–508 (2009). Elsevier
Li, C., Cai, X., Lee, C.: Scheduling with multiple-job-on-one-processor pattern. IIE Trans. 30(5), 433–445 (1998). Springer
Lim, A.: The berth planning problem. Oper. Res. Lett. 22(2), 105–110 (1998). Elsevier
Moon, K.: A mathematical model and a heuristic algorithm for berth planning. Brain Korea. 21 (2000). Citeseer
Moorthy, R., Teo, C.: Berth management in container terminal: the template design problem. OR Spectrum 28(4), 495–518 (2006). Springer
Kaveshgar, N., Huynh, N., Rahimian, S.: An efficient genetic algorithm for solving the quay crane scheduling problem. Expert Syst. Appl. 39(18), 13108–13117 (2012). Elsevier
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, New York (2011)
Kim, K., Moon, K.: Berth scheduling by simulated annealing. Transp. Res. Part B Methodol. 37(6), 541–560 (2003). Elsevier
Vazquez-Rodríguez, J.A., Salhi, A.: Hybrid evolutionary methods for the solutionof complex scheduling problems. In: Advances in Artificial Intelligence, pp. 17–28 (2006)
Salhi, A., Fraga, E.S.: Nature-inspired optimisation approaches and the new plant propagation algorithm. In: Proceedings of the ICeMATH 2011, pp. K2-1-K2-8 (2011)
Vazquez-Rodríguez, J.A., Salhi, A.: A synergy exploiting evolutionary approach to complex scheduling problems. In: Computer Aided Methods in Optimal Design and Operations, Series on Computers and Operations Research, pp. 59–68. World Scientific (2006)
Salhi, A., Vazquez-Rodríguez, J.A.: Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions. Memetic Comput. 6(2), 77–84 (2014). Springer
Wang, F., Lim, A.: A stochastic beam search for the berth allocation problem. Dec. Supp. Syst. 42(4), 2186–2196 (2007). Elsevier
Xu, Y., Chen, Q., Quan, X.: Robust berth scheduling with uncertain vessel delay and handling time. Ann. Oper. Res. 192(1), 123–140 (2012). Springer
Zhen, L., Chang, D.: A bi-objective model for robust berth allocation scheduling. Comput. Ind. Eng. 63(1), 262–273 (2012). Elsevier
Zhen, L., Lee, L., Chew, E.: A decision model for berth allocation under uncertainty. Eur. J. Oper. Res. 212(1), 54–68 (2011). Elsevier
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Alsoufi, G., Yang, X., Salhi, A. (2016). Robust Berth Allocation Using a Hybrid Approach Combining Branch-and-Cut and the Genetic Algorithm. In: Blesa, M., et al. Hybrid Metaheuristics. HM 2016. Lecture Notes in Computer Science(), vol 9668. Springer, Cham. https://doi.org/10.1007/978-3-319-39636-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-39636-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39635-4
Online ISBN: 978-3-319-39636-1
eBook Packages: Computer ScienceComputer Science (R0)