Simulation Model of Internal Transportation at a Container Terminal to Determine the Number of Vehicles Required

  • Carlos J. Uribe-Martes
  • Doris Xiomara Rivera-Restrepo
  • Angélica Borja-Di Filippo
  • Jesús SilvaEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)


The operating efficiency of a container terminal is largely determined by the number of vehicles available for internal transportation. This article presents a discrete event simulation model, combined with scenario analysis, to help determine the adequate number of vehicles to satisfy the demand for internal container movements at a port in the city of Barranquilla. The model assesses the container movements performed by Straddle Carriers (SC) between the container loading/unloading dock and the storage and inspection yards. The results of the experiments performed indicate that when demand increases by more than 10%, the number of vehicles currently available may be insufficient to cover operating requirements in an efficient manner. The simulation model tests the effectiveness of a set of strategies that may be implemented at the studied terminal.


Scenario analysis Container terminal Discrete event simulation Straddle Carriers 


  1. 1.
    Carlo, H.J., Vis, I.F.A., Roodbergen, K.J.: Transport operations in container terminals: Literature overview, trends, research directions and classification scheme. Eur. J. Oper. Res. 236(1), 1–13 (2014)CrossRefGoogle Scholar
  2. 2.
    Dkhil, H., Yassine, A., Chabchoub, H.: Multi-objective optimization of the integrated problem of location assignment and straddle carrier scheduling in maritime container terminal at import. J. Oper. Res. Soc. 69(2), 247–269 (2018)CrossRefGoogle Scholar
  3. 3.
    Canonaco, P., Legato, P., Mazza, R.M., Musmanno, R.: A queuing network model for the management of berth crane operations. Comput. Oper. Res. 35(8), 2432–2446 (2008)CrossRefGoogle Scholar
  4. 4.
    Zehendner, E., Rodriguez-Verjan, G., Absi, N., Dauzère-Pérès, S., Feillet, D.: Optimized allocation of straddle carriers to reduce overall delays at multimodal container terminals. Flex. Serv. Manuf. J. 27(2–3), 300–330 (2015)CrossRefGoogle Scholar
  5. 5.
    Zeng, Q., Yang, Z.: Integrating simulation and optimization to schedule loading operations in container terminals. Comput. Oper. Res. 36(6), 1935–1944 (2009)CrossRefGoogle Scholar
  6. 6.
    Al-Dhaheri, N., Jebali, A., Diabat, A.: A simulation-based Genetic Algorithm approach for the quay crane scheduling under uncertainty. Simul. Model. Pract. Theor. 66, 122–138 (2016)CrossRefGoogle Scholar
  7. 7.
    Soriguera, F., Robuste, F., Juanola, R., Lopez-Pita, A.: optimization of handling equipment in the container terminal of the port of Barcelona, Spain. Transp. Res. Rec.: J. Transp. Res. Board 1963, 44–51 (2006)CrossRefGoogle Scholar
  8. 8.
    Legato, P., Mazza, R.M.: A simulation model for designing straddle carrier-based container terminals. In: 2017 Winter Simulation Conference (WSC), pp. 3138–3149. IEEE (2017)Google Scholar
  9. 9.
    Wiese, J., Suhl, L., Kliewer, N.: An analytical model for designing yard layouts of a straddle carrier based container terminal. Flex. Serv. Manuf. J. 25(4), 466–502 (2013)CrossRefGoogle Scholar
  10. 10.
    Orejuela Cabrera, J.P., Flórez González, A.: Balanceo de líneas de producción en la industria farmacéutica mediante Programación por metas. INGE CUC 15(1), 109–122 (2019)CrossRefGoogle Scholar
  11. 11.
    Romero-Conrado, A., Coronado-Hernandez, J., Rius-Sorolla, G., García-Sabater, J.: A tabu list-based algorithm for capacitated multilevel lot-sizing with alternate bills of materials and co-production environments. Appl. Sci. 9(7), 1464 (2019)CrossRefGoogle Scholar
  12. 12.
    Varela, N., Fernandez, D., Pineda, O., Viloria, A.: Selection of the best regression model to explain the variables that influence labor accident case electrical company. J. Eng. Appl. Sci. 12(1), 2956–2962 (2017)Google Scholar
  13. 13.
    Banks, J., Carson II, J.S., Nelson, B.L,. Nicol, D.M.: Discrete-Event System Simulation, 5th edn. Pearson, London (2014)Google Scholar
  14. 14.
    Law, A.M.: Simulation Modeling and Analysis, 5th edn. McGraw-Hill, New York (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Carlos J. Uribe-Martes
    • 1
  • Doris Xiomara Rivera-Restrepo
    • 2
  • Angélica Borja-Di Filippo
    • 3
  • Jesús Silva
    • 4
    Email author
  1. 1.Universidad de la CostaBarranquillaColombia
  2. 2.Groupe RobertLongueuilCanada
  3. 3.Universidad Autónoma del CaribeBarranquillaColombia
  4. 4.Universidad Peruana de Ciencias AplicadasLimaPeru

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