Understanding the Impact of User Behaviours and Scheduling Parameters on the Effectiveness of a Terminal Appointment System Using Discrete Event Simulation

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 567)


This research improves understanding of the impact of specific types of truck driver behaviour and temporal scheduling on the effectiveness of a terminal appointment system. A discrete event simulation model of a bulk cargo marine terminal is developed to analyse parameters related to driver behaviour (punctuality and proportion of planned appointments) and temporal scheduling (appointments per time window and time window spacing) on truck flows and turnaround times at the terminal. The model is based on an Australian wood chip export marine terminal currently experiencing significant truck congestion. The terminal operator and stakeholders have expressed interest in the implementation of an appointment system to address this issue. The modelling presented in this research was used to inform their investigation into developing an appointment system solution.

Simulation results indicate that the proportion of planned appointments, used as a proxy for the appointment system use, has a significant impact on truck turnaround times. Greater truck arrival punctuality only marginally improves truck turnaround times. Interestingly most optimization approaches continue to focus on improving punctuality through service rules or financial penalties in order to achieve optimal turnaround times. However, the additional cost in terms of complexity or assumptions for optimal solutions against non-optimal approaches are rarely weighed in terms of dividends of the marginal improvements generated. By involving terminal users (drivers and transporters) in the design of an appointment system and its scheduling parameters, terminal operators can significantly improve appointment system use and effectiveness by increasing the probability of positive users’ behaviours.


Transport management Supply chain collaboration User requirements Congestion management 


  1. 1.
    Chen, G., Govindan, K., Yang, Z.: Managing truck arrivals with time windows to alleviate gate congestion at container terminals. Int. J. Prod. Econ. 141, 179–188 (2013)CrossRefGoogle Scholar
  2. 2.
    Torkjazi, M., Huynh, N., Shiri, S.: Truck appointment systems considering impact to drayage truck tours. Transp. Res. Part E Logist. Transp. Rev. 116, 208–228 (2018)CrossRefGoogle Scholar
  3. 3.
    Huynh, N., Walton, C.M.: Robust scheduling of truck arrivals at marine container terminals. J. Transp. Eng. 134, 347–353 (2008)CrossRefGoogle Scholar
  4. 4.
    Li, N., Chen, G., Govindan, K., Jin, Z.: Disruption management for truck appointment system at a container terminal: a green initiative. Transp. Res. Part D Transp. Environ. 61, 261–273 (2018)CrossRefGoogle Scholar
  5. 5.
    Ramírez-Nafarrate, A., González-Ramírez, R.G., Smith, N.R., Guerra-Olivares, R., Voß, S.: Impact on yard efficiency of a truck appointment system for a port terminal. Ann. Oper. Res. 258, 195–216 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen, G., Jiang, L.: Managing customer arrivals with time windows: a case of truck arrivals at a congested container terminal. Ann. Oper. Res. 244, 349–365 (2016)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Guan, C., Liu, R.: Modeling gate congestion of marine container terminals, truck waiting cost, and optimization. Transp. Res. Rec. J. Transp. Res. Board 2100, 58–67 (2009)CrossRefGoogle Scholar
  8. 8.
    Huynh, N., Walton, M.: Improving efficiency of drayage operations at seaport container terminals through the use of an appointment system. In: Böse, J. (ed.) Handbook of Terminal Planning, pp. 323–344. Springer, New York (2011). Scholar
  9. 9.
    Morais, P., Lord, E.: Terminal appointment system study. Transp. Res. Board 1, 123 (2006)Google Scholar
  10. 10.
    Huynh, N., Smith, D., Harder, F.: Truck appointment systems. Transp. Res. Rec. J. Transp. Res. Board 2548, 1–9 (2016)CrossRefGoogle Scholar
  11. 11.
    Ackoff, R.: The Art of Problem Solving. Wiley, New York (1978)Google Scholar
  12. 12.
    Neagoe, M., Taskhiri, M.S., Nguyen, H.-O., Hvolby, H.-H., Turner, P.: Exploring congestion impact beyond the bulk cargo terminal gate. In: Logistics 4.0 and Sustainable Supply Chain Management, Proceedings of HICL 2018, pp. 63–82 (2018)Google Scholar
  13. 13.
    Neagoe, M., Taskhiri, M.S., Nguyen, H.-O., Turner, P.: Exploring the role of information systems in mitigating gate congestion using simulation: theory and practice at a bulk export terminal gate. In: Moon, I., Lee, G.M., Park, J., Kiritsis, D., von Cieminski, G. (eds.) APMS 2018. IFIPAICT, vol. 535, pp. 367–374. Springer, Cham (2018). Scholar
  14. 14.
    Guan, C., Liu, R.: Container terminal gate appointment system optimization. Marit. Econ. Logist. 11, 378–398 (2009)CrossRefGoogle Scholar
  15. 15.
    Chen, G., Govindan, K., Yang, Z.Z., Choi, T.M., Jiang, L.: Terminal appointment system design by non-stationary M(t)/E k/c(t) queueing model and genetic algorithm. Int. J. Prod. Econ. 146, 694–703 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.ARC Centre for Forest Value, Discipline of ICT, College of Sciences and EngineeringUniversity of TasmaniaHobartAustralia
  2. 2.Centre for Logistics, Department of Materials and ProductionAalborg UniversityAalborgDenmark
  3. 3.Department of Mechanical and Industrial EngineeringNorwegian University of Science and TechnologyTrondheimNorway

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