Skip to main content
Log in

Management of internal delivery vehicles in maritime container terminals

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://unctad.org.

References

  1. Alguwaizani, A., Hansen, P., Mladenović, N., Ngai, E.: Variable neighborhood search for harmonic means clustering. Appl. Math. Model. 35(6), 2688–2694 (2011)

    Article  MATH  Google Scholar 

  2. Beasley, J.E.: A population heuristic for constrained two-dimensional non-guillotine cutting. Eur. J. Oper. Res. 156(3), 601–627 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  4. Birattari,M.: Tuning metaheuristics: A Machine Learning Perspective. 1st edn. 2005. 2nd printing edn. Springer, Berlin (2009)

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  9. Daniel, W.W.: Applied Nonparametric Statistics. PWS-Kent Publishing Company, Boston (1990)

    Google Scholar 

  10. Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  11. De Jong, K.: Parameter Setting in EAs: A 30 Year Perspective. Springer, Berlin (2007)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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 12

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. Hansen, P., Vukicević, D.: Variable neighborhood search for extremal graphs. 23. On the randi index and the chromatic number. Discret. Math. 309, 42284234 (2009)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  22. Jarboui, B., Derbel, H., Hanafi, S., Mladenovi, N.: Variable neighborhood search for location routing. Comput. Oper. Res. 40(1), 47–57 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  26. Legato, P., Trunfio, R., Meisel, F.: Modeling and solving rich quay crane scheduling problems. Comput. Oper. Res. 39(9), 2063–2078 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  32. Reeves, C.R.: Genetic algorithms for the operations researcher. J. Comput. 9(3), 231–250 (1997)

    MathSciNet  MATH  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  36. Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall, CRC (2007)

    MATH  Google Scholar 

  37. Tao, J., Qiu, Y.: A simulation optimization method for vehicles dispatching among multiple container terminals. Expert Syst. Appl. 42(7), 3742–3750 (2015)

    Article  Google Scholar 

  38. Ullrich, G.: Automated Guided Vehicle Systems. A Primer with Practical Applications. Springer, Berlin (2015)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  40. Vis, I.F.A., Harika, I.: Comparison of vehicle types at an automated container terminal. OR Spectr. 26(1), 117–143 (2004)

    Article  MATH  Google Scholar 

  41. Zeng, J., Hsu, W.J.: Conflict-free container routing in mesh yard layouts. Robot. Auton. Syst. 56(5), 451–460 (2008)

    Article  Google Scholar 

  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)

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Marcos Moreno-Vega.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

López-Plata, I., Expósito-Izquierdo, C., Melián-Batista, B. et al. Management of internal delivery vehicles in maritime container terminals. Prog Artif Intell 7, 65–80 (2018). https://doi.org/10.1007/s13748-017-0129-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-017-0129-1

Keywords

Navigation