Abstract
In this paper we present a novel hybrid algorithm, in which ideas from the genetic algorithm and the GRASP metaheuristic are cooperatively used and intertwined to dynamically adjust a key parameter of the corridor method, i.e., the corridor width, during the search process. In addition, a fine-tuning technique for the corridor method is then presented. The response surface methodology is employed in order to determine a good set of parameter values given a specific problem input size. The effectiveness of both the algorithm and the validation of the fine tuning technique are illustrated on a specific problem selected from the domain of container terminal logistics, known as the blocks relocation problem, where one wants to retrieve a set of blocks from a bay in a specified order, while minimizing the overall number of movements and relocations. Computational results on 160 benchmark instances attest the quality of the algorithm and validate the fine tuning process.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sniedovich, M., Voß, S.: The corridor method: a dynamic programming inspired metaheuristic. Control and Cybernetics 35(3), 551–578 (2006)
Caserta, M., Voß, S.: A cooperative strategy for guiding the corridor method. In: Kacprzyk, J. (ed.) Studies in Computational Intelligence. Springer, Heidelberg (2009)
Ergun, O., Orlin, J.: A dynamic programming methodology in very large scale neighborhood search applied to the traveling salesman problem. Discrete Optimization 3, 78–85 (2006)
Potts, C., van de Velde, S.: Dynasearch - iterative local improvement by dynamic programming. Technical report, University of Twente (1995)
Yang, J.H., Kim, K.H.: A grouped storage method for minimizing relocations in block stacking systems. Journal of Intelligent Manufacturing 17, 453–463 (2006)
Kim, K.H., Hong, G.P.: A heuristic rule for relocating blocks. Computers & Operations Research 33, 940–954 (2006)
Caserta, M., Voß, S., Sniedovich, M.: An algorithm for the blocks relocation problem. Working Paper, Institute of Information Systems, University of Hamburg (2008)
Stahlbock, R., Voß, S.: Operations research at container terminals: a literature update. OR Spectrum 30, 1–52 (2008)
Watanabe, I.: Characteristics and analysis method of efficiencies of container terminal: an approach to the optimal loading/unloading method. Container Age 3, 36–47 (1991)
Castilho, B., Daganzo, C.: Handling strategies for import containers at marine terminals. Transportation Research B 27(2), 151–166 (1993)
Kim, K.H.: Evaluation of the number of rehandles in container yards. Computers & Industrial Engineering 32(4), 701–711 (1997)
Kim, K.H., Park, Y.M., Ryu, K.R.: Deriving decision rules to locate export containers in container yards. European Journal of Operational Research 124, 89–101 (2000)
Hart, J., Shogan, A.: Semi-greedy heuristics: an empirical study. Operations Research Letters 6, 107–114 (1987)
Festa, P., Resende, M.: An annotated bibliography of GRASP. Technical report, AT&T Labs Research (2004)
Box, G., Wilson, K.: On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society Series B - 13, 1–45 (1951)
Caserta, M., Quiñonez, E.: A cross entropy-Lagrangean hybrid algorithm for the multi-item capacitated lot-sizing problem with setup times. Computers & Operations Research 36(2), 530–548 (2009)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Caserta, M., Voß, S. (2009). Corridor Selection and Fine Tuning for the Corridor Method. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-11169-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11168-6
Online ISBN: 978-3-642-11169-3
eBook Packages: Computer ScienceComputer Science (R0)