Advertisement

Progress in Artificial Intelligence

, Volume 7, Issue 1, pp 65–80 | Cite as

Management of internal delivery vehicles in maritime container terminals

  • Israel López-Plata
  • Christopher Expósito-Izquierdo
  • Belén Melián-Batista
  • J. Marcos Moreno-Vega
Regular Paper
  • 127 Downloads

Abstract

Maritime container terminals are complex infrastructures to manage in transportation industry due to their high degree of uncertainty arisen from the limited and changing information. The present paper addresses the operational management of the available internal delivery vehicles on the yard of a maritime container terminal under random changes in the simultaneous movement of import, export, and transit containers. The main goal of the presented problem is to optimize the usage of the available internal vehicles in terms of working time in scenarios where synchronization is required when accessing to the different pick-up and drop-off container locations. An efficient variable neighbourhood search is here proposed to dispatch, route, and schedule the existing vehicles while adapting their behaviour to both the arrival of new information and unforeseen changes in the existing information related to the environment under analysis. The computational experiments indicate the suitable performance of the proposed technique on a wide range of realistic scenarios.

Keywords

Variable neighbourhood search Internal delivery vehicle Maritime container terminal 

Notes

Acknowledgements

This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with FEDER funds (Projects TIN2012-32608 and TIN2015-70226-R).

References

  1. 1.
    Alguwaizani, A., Hansen, P., Mladenović, N., Ngai, E.: Variable neighborhood search for harmonic means clustering. Appl. Math. Model. 35(6), 2688–2694 (2011)CrossRefMATHGoogle Scholar
  2. 2.
    Beasley, J.E.: A population heuristic for constrained two-dimensional non-guillotine cutting. Eur. J. Oper. Res. 156(3), 601–627 (2004)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Berbeglia, G., Cordeau, J.-F., Gribkovskaia, I., Laporte, G.: Static pickup and delivery problems: a classification scheme and survey. TOP 15(1), 1–31 (2007)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Birattari,M.: Tuning metaheuristics: A Machine Learning Perspective. 1st edn. 2005. 2nd printing edn. Springer, Berlin (2009)Google Scholar
  5. 5.
    Bish, E.K., Chen, F.Y., Leong, Y.T., Nelson, B.L., Ng, J.W.C., Simchi-Levi, D.: Dispatching vehicles in a mega container terminal. OR Spectr. 27(4), 491–506 (2005)CrossRefMATHGoogle Scholar
  6. 6.
    Blazewicz, J., Burkard, R.E., Finke, G., Woeginger, G.J.: Vehicle scheduling in two-cycle flexible manufacturing systems. Math. Comput. Model. 20(2), 19–31 (1994)CrossRefMATHGoogle Scholar
  7. 7.
    Caporossia, G., Gutmanb, I., Hansen, P.: Variable neighborhood search for extremal graphs: Iv: chemical trees with extremal connectivity index. Comput. Chem. 23, 469477 (1999)Google Scholar
  8. 8.
    Coy, S.P., Golden, B.L., Runger, G.C., Wasil, E.A.: Using experimental design to find effective parameter settings for heuristics. J. Heuristics 7(1), 77–97 (2001)CrossRefMATHGoogle Scholar
  9. 9.
    Daniel, W.W.: Applied Nonparametric Statistics. PWS-Kent Publishing Company, Boston (1990)Google Scholar
  10. 10.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    De Jong, K.: Parameter Setting in EAs: A 30 Year Perspective. Springer, Berlin (2007)Google Scholar
  12. 12.
    Ding, D., Chou, M.C.: Stowage planning for container ships: a heuristic algorithm to reduce the number of shifts. Eur. J. Oper. Res. 246(1), 242–249 (2015)CrossRefMATHGoogle Scholar
  13. 13.
    El Khayat, G., Langevin, A., Riopel, D.: Integrated production and material handling scheduling using mathematical programming and constraint programming. Eur. J. Oper. Res. 175(3), 1818–1832 (2006)CrossRefMATHGoogle Scholar
  14. 14.
    Eskandarpour, M., Zegordi, S.H., Nikbakhsh, E.: A parallel variable neighborhood search for the multi-objective sustainable post-sales network design problem. Int. J. Prod. Econ. 145(1), 117–131 (2013)CrossRefMATHGoogle Scholar
  15. 15.
    Fleming, C.L., Griffis, S.E., Bell, J.E.: The effects of triangle inequality on the vehicle routing problem. Eur. J. Oper. Res. 224(1), 1–7 (2013)CrossRefGoogle Scholar
  16. 16.
    Fleszar, K., Osman, I.H., Hindi, K.S.: A variable neighbourhood search algorithm for the open vehicle routing problem. Eur. J. Oper. Res. 195(3), 803–809 (2009)CrossRefMATHGoogle Scholar
  17. 17.
    Fransoo, J.C., Lee, C.-Y.: The critical role of ocean container transport in global supply chain performance. Prod. Oper. Manag. 22(2), 253–268 (2013). cited By 12CrossRefGoogle Scholar
  18. 18.
    García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the cec’2005 special session on real parameter optimization. J. Heuristics 15, 617–644 (2009)CrossRefMATHGoogle Scholar
  19. 19.
    Hansen, P., Mladenovi, N., Brimberg, J., JosA, M.P.: Variable neighborhood search. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics Volume 146 of International Series in Operations Research & Management Science, pp. 61–86. Springer, Berlin (2010)Google Scholar
  20. 20.
    Hansen, P., Vukicević, D.: Variable neighborhood search for extremal graphs. 23. On the randi index and the chromatic number. Discret. Math. 309, 42284234 (2009)CrossRefMATHGoogle Scholar
  21. 21.
    Homayouni, S.M., Tang, S.H., Motlagh, O.: A genetic algorithm for optimization of integrated scheduling of cranes, vehicles, and storage platforms at automated container terminals. J. Comput. Appl. Math. 270, 545–556 (2014)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Jarboui, B., Derbel, H., Hanafi, S., Mladenovi, N.: Variable neighborhood search for location routing. Comput. Oper. Res. 40(1), 47–57 (2013)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Jiang, X., Chew, E.P., Lee, L.H.: Innovative container terminals to improve global container transport chains. In: Lee, C.Y., Meng, Q. (eds.) Handbook of Ocean Container Transport Logistics, volume 220 of International Series in Operations Research & Management Science, pp. 3–41. Springer, Berlin (2015)Google Scholar
  24. 24.
    Kim, K.H., Bae, J.W.: A look-ahead dispatching method for automated guided vehicles in automated port container terminals. Transp. sci. 38(2), 224–234 (2004)CrossRefGoogle Scholar
  25. 25.
    Kim, K.H., Jeon, S.M., Ryu, K.R.: Deadlock prevention for automated guided vehicles in automated container terminals. OR Spectr. 28(4), 659–679 (2006)CrossRefMATHGoogle Scholar
  26. 26.
    Legato, P., Trunfio, R., Meisel, F.: Modeling and solving rich quay crane scheduling problems. Comput. Oper. Res. 39(9), 2063–2078 (2012)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Lim, K.J., Kim, H.K., Yoshimoto, K., Lee, H.J., Takahashi, T.: A dispatching method for automated guided vehicles by using a bidding concept. OR Spectr. 25(1), 25–44 (2003)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Luo, J., Wu, Y.: Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals. Transp. Res. E: Logist. Transp Rev. 79, 49–64 (2015)CrossRefGoogle Scholar
  29. 29.
    Mjirda, A., Todosijević, R., Hanafic, S., Hansen, P., Mladenović, N.: Sequential variable neighborhood descent variants: an empirical study on the traveling salesman problem. Int. Trans. Oper. Res. 24, 615–633 (2016)Google Scholar
  30. 30.
    Nishimura, E., Imai, A., Papadimitriou, S.: Berth allocation planning in the public berth system by genetic algorithms. Eur. J. Oper. Res. 131(2), 282–292 (2001)CrossRefMATHGoogle Scholar
  31. 31.
    Queiroz dos Santos, J.P., de Melo, J.D., Duarte-Neto, D., Aloise, D.: Reactive search strategies using reinforcement learning, local search algorithms and variable neighborhood search. Expert Syst. Appl. 41(10), 4939–4949 (2014)CrossRefGoogle Scholar
  32. 32.
    Reeves, C.R.: Genetic algorithms for the operations researcher. J. Comput. 9(3), 231–250 (1997)MathSciNetMATHGoogle Scholar
  33. 33.
    Resende, M.G.C., Ribeiro, C.C.: Search methodologies: introductory tutorials in optimization and decision support techniques. In: Burke, E.K., Kendall, G. (eds.) Chapter GRASP: Greedy Randomized Adaptive Search Procedures, pp. 287–312. Springer, Boston (2014)Google Scholar
  34. 34.
    Roshanaei, V., Naderi, B., Jolai, F., Khalili, M.: A variable neighborhood search for job shop scheduling with set-up times to minimize makespan. Future Gener. Comput. Syst. 25(6), 654–661 (2009)Google Scholar
  35. 35.
    Saidi-Mehrabad, M., Dehnavi-Arani, S., Evazabadian, F., Mahmoodian, V.: An ant colony algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput. Ind. Eng. 86, 2–13 (2015)CrossRefGoogle Scholar
  36. 36.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall, CRC (2007)MATHGoogle Scholar
  37. 37.
    Tao, J., Qiu, Y.: A simulation optimization method for vehicles dispatching among multiple container terminals. Expert Syst. Appl. 42(7), 3742–3750 (2015)CrossRefGoogle Scholar
  38. 38.
    Ullrich, G.: Automated Guided Vehicle Systems. A Primer with Practical Applications. Springer, Berlin (2015)Google Scholar
  39. 39.
    Vis, I.F.A., de Koster, R.: Transshipment of containers at a container terminal: an overview. Eur. J. Oper. Res. 147(1), 1–16 (2003)CrossRefMATHGoogle Scholar
  40. 40.
    Vis, I.F.A., Harika, I.: Comparison of vehicle types at an automated container terminal. OR Spectr. 26(1), 117–143 (2004)CrossRefMATHGoogle Scholar
  41. 41.
    Zeng, J., Hsu, W.J.: Conflict-free container routing in mesh yard layouts. Robot. Auton. Syst. 56(5), 451–460 (2008)CrossRefGoogle Scholar
  42. 42.
    Zhao, N., Xia, M., Mi, C., Bian, Z., Jin, J., Gasparetto, A.: Simulation-based optimization for storage allocation problem of outbound containers in automated container terminals. Math. Probl. Eng. 2015, 1–14 (2015)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering and SystemsUniversidad de La LagunaSan Cristóbal de La LagunaSpain

Personalised recommendations