Advertisement

Maritime Economics & Logistics

, Volume 21, Issue 1, pp 125–145 | Cite as

Optimization of truck appointments in container terminals

  • Xiaoju Zhang
  • Qingcheng ZengEmail author
  • Zhongzhen Yang
Original Article
  • 93 Downloads

Abstract

Truck appointment has proved to be an efficient tool in reducing congestion at container terminals. To make a reasonable appointment quota plan, it is necessary to take terminal operations into consideration. We develop a novel approach (model) for optimizing a truck appointment system with the objective of decreasing external trucks’ waiting times, at the gate and yard, and internal trucks’ waiting times at the yard. The vacation queuing model is used to describe the coordinated service process of yard cranes. Based on non-stationary queuing theory, truck waiting times are estimated more accurately. Numerical experiments are conducted to illustrate the validity of the model and algorithm. Results show that the model reflects the characteristics of the service process of yard cranes and it improves the calculation accuracy of the truck waiting time.

Keywords

Container terminals Truck appointment system Internal schedules Non-stationary queuing theory 

Notes

Acknowledgements

The authors would like to thank the anonymous referees and editor-in-chief for their careful reading and constructive suggestions. This work is supported by the National Natural Science Foundation of China [Grant Nos. 71671021 and 71431001], and Fundamental Research Funds for the Central Universities (Grant No. 3132016302).

References

  1. Arnott, R., and K. Small. 1994. The economics of traffic congestion. American Scientist 20 (2): 123–127.Google Scholar
  2. Chen, G., K. Govindan, and Z.Z. Yang. 2013a. Managing truck arrivals with time windows to alleviate gate congestion at container terminals. International Journal of Production Economics 141 (1): 179–188.CrossRefGoogle Scholar
  3. Chen, G., K. Govindan, Z.Z. Yang, et al. 2013b. Terminal appointment system design by non-stationary M(t)/Ek/c(t) queuing model and genetic algorithm. International Journal of Production Economics 146 (2): 694–703.CrossRefGoogle Scholar
  4. Chen, G., K. Govindan, et al. 2013c. Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern. Transportation Research Part E : Logistics and Transportation Review 2013 (55): 3–22.CrossRefGoogle Scholar
  5. Chen, X., X. Zhou, and G.F. List. 2011. Using time-varying tolls to optimize truck arrivals at ports. Transportation Research Part E 47 (6): 965–982.CrossRefGoogle Scholar
  6. Cosmetatos, G.P. 1976. Some approximate equilibrium results for the multi-server queue (M/G/r). Operational Research Quarterly 27 (3): 615–620.CrossRefGoogle Scholar
  7. Douma, A., M. Schutten, and P. Schuur. 2009. Waiting profiles: An efficient protocol for enabling distributed planning of container barge rotations along terminals in Rotterdam. Transportation Research Part C 17: 133–148.CrossRefGoogle Scholar
  8. Giulianoa, G., and T. O’Brien. 2007. Reducing port-related truck emissions: The terminal gate appointment system at the Ports of Los Angeles and Long Beach. Transportation Research Part D 12 (7): 460–473.CrossRefGoogle Scholar
  9. Guan, C.Q., and R.F. Liu. 2009. Container terminal gate appointment system optimization. Maritime Economics & Logistics 11: 378–398.CrossRefGoogle Scholar
  10. Huynh, N., and C.M. Walton. 2008. Robust scheduling of truck arrivals at maritime container terminals. Journal of Transportation Engineering 134 (8): 347–353.CrossRefGoogle Scholar
  11. Huynh, N., and C.M. Walton. 2011. Improving efficiency of drayage operations at seaport container terminals through the use of an appointment system. In Handbook of terminal planning, ed. J.W. Bose, 323–344. New York: Springer.CrossRefGoogle Scholar
  12. Ke, J. 2003. The analysis of a general input with N-policy and exponential vacations. Queuing Sys. 45: 135–160.CrossRefGoogle Scholar
  13. LaGanga, L.R., and S.R. Lawrence. 2012. Appointment overbooking in health care clinics to improve patient service and clinic performance. Production and Operations Management 21 (5): 874–888.CrossRefGoogle Scholar
  14. Levy, Y., and U. Yechiali. 1975. Utilization of idle time in an M/G/1 queuing system. Management Science 22 (2): 202–211.CrossRefGoogle Scholar
  15. Li, N., et al. 2016. Disruption management for truck appointment system at a container terminal: A green initiative. Transportation Research Part D (in press).Google Scholar
  16. Liu, N., S. Ziya, and V.G. Kulkarni. 2010. Dynamic scheduling of outpatient appointments under patient no-shows and cancelations. Manufacture and Service Operations Management 12 (2): 347–364.CrossRefGoogle Scholar
  17. Morais, P., and E. Lord. 2006. Terminal appointment system study. Transport Canada, TP 14570E, Ottawa.Google Scholar
  18. Namboothiri, R., and A.L. Erera. 2008. Planning local container drayage operations given a port access appointment system. Transportation Research Part E: Logistics and Transportation Review 44 (2): 185–202.CrossRefGoogle Scholar
  19. Ozbay, K., O. Yanmaz-Tuzel, and J. Holguín-Veras. 2006. The impacts of time-of-day pricing initiative at NY/NJ port authority facilities car and truck movements. Transportation Research Record: Journal of the Transportation Research Board 1853: 48–56.Google Scholar
  20. Phan, M.H., and K.H. Kim. 2015. Negotiating truck arrival times among trucking companies and a container terminal. Transportation Research Part E Logistics & Transportation Review 75: 132–144.CrossRefGoogle Scholar
  21. Phan, M.H., K.H. Kim, and F. Mannering. 2016. Collaborative truck scheduling and appointments for trucking companies and container terminals. Transportation Research Part B Methodological 86: 37–50.CrossRefGoogle Scholar
  22. The United Nations Conference on Trade and Development. http://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=13321.
  23. Tian, N., and X.L. Xu. 2005. The waiting time of an M/M/c queue with partial servers vacations. OR Transactions. 9 (2): 1–8.Google Scholar
  24. Tian, N., Q.L. Li, and J. Gao. 1999. Conditional stochastic decomposition in M/M/c queue with server vacations. Communications in Statistics Part C Stochastic Models 15 (2): 367–377.CrossRefGoogle Scholar
  25. Wang, W.-P., D. Tipper, and S. Banerjee. 1996. A simple approximation for modeling nonstationary queues. In: Fifteenth annual joint conference of the IEEE computer societies. Networking the next generation. Proceedings IEEE, INFOCOM ’96, vol. 1, 255–262.Google Scholar
  26. Zehendner, E., and D. Feillet. 2014. Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal. European Journal of Operational Research 235 (235): 461–469.CrossRefGoogle Scholar
  27. Zhao, W., and A.V. Goodchild. 2010. The impact of truck arrival information on container terminal rehandling. Transportation Research Part E: Logistics and Transportation Review 46 (3): 327–343.CrossRefGoogle Scholar
  28. Zhang, X.J., Q.C. Zeng, and W.H. Chen. 2013. Optimization model for truck appointment in container terminals. Procedia-Social and Behavioral Sciences 96: 1938–1947.CrossRefGoogle Scholar
  29. Zhang, Z.G., and N. Tian. 2003. Analysis on queueing systems with synchronous vacations of partial servers. Performance Evaluation 52 (4): 269–282.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Maritime Economics and ManagementDalian Maritime UniversityDalianPeople’s Republic of China
  2. 2.Faculty of Maritime and TransportationNingbo UniversityNingboPeople’s Republic of China
  3. 3.National Traffic Management Engineering & Technology Research Centre Ningbo University Sub-centreNingboPeople’s Republic of China

Personalised recommendations