Corridor Selection and Fine Tuning for the Corridor Method

  • Marco Caserta
  • Stefan Voß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5851)


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.


Response Surface Methodology Fine Tune Incumbent Solution Target Block Elite Solution 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sniedovich, M., Voß, S.: The corridor method: a dynamic programming inspired metaheuristic. Control and Cybernetics 35(3), 551–578 (2006)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Caserta, M., Voß, S.: A cooperative strategy for guiding the corridor method. In: Kacprzyk, J. (ed.) Studies in Computational Intelligence. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    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)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Potts, C., van de Velde, S.: Dynasearch - iterative local improvement by dynamic programming. Technical report, University of Twente (1995)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Kim, K.H., Hong, G.P.: A heuristic rule for relocating blocks. Computers & Operations Research 33, 940–954 (2006)zbMATHCrossRefGoogle Scholar
  7. 7.
    Caserta, M., Voß, S., Sniedovich, M.: An algorithm for the blocks relocation problem. Working Paper, Institute of Information Systems, University of Hamburg (2008)Google Scholar
  8. 8.
    Stahlbock, R., Voß, S.: Operations research at container terminals: a literature update. OR Spectrum 30, 1–52 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Castilho, B., Daganzo, C.: Handling strategies for import containers at marine terminals. Transportation Research B 27(2), 151–166 (1993)CrossRefGoogle Scholar
  11. 11.
    Kim, K.H.: Evaluation of the number of rehandles in container yards. Computers & Industrial Engineering 32(4), 701–711 (1997)CrossRefGoogle Scholar
  12. 12.
    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)zbMATHCrossRefGoogle Scholar
  13. 13.
    Hart, J., Shogan, A.: Semi-greedy heuristics: an empirical study. Operations Research Letters 6, 107–114 (1987)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Festa, P., Resende, M.: An annotated bibliography of GRASP. Technical report, AT&T Labs Research (2004)Google Scholar
  15. 15.
    Box, G., Wilson, K.: On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society Series B - 13, 1–45 (1951)Google Scholar
  16. 16.
    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)zbMATHCrossRefGoogle Scholar
  17. 17.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marco Caserta
    • 1
  • Stefan Voß
    • 1
  1. 1.Institute of Information SystemsUniversity of HamburgHamburgGermany

Personalised recommendations