Advertisement

Scheduling appointments for container truck arrivals considering their effects on congestion

  • Sanghyuk Yi
  • Bernd Scholz-Reiter
  • Taehoon Kim
  • Kap Hwan KimEmail author
Article
  • 2 Downloads

Abstract

Trucking companies deliver a large number of containers every day to container terminals at hub ports. Truck drivers for the delivery operation can experience long waiting times when they arrive at peak hours. This study proposes a scheduling method for appointments that considers the cost of trucks staying in the terminal, demurrage cost, container delivery cost, number of appointments allowed at each time window and block, and number of trucks available during each time window. Unlike previous studies, this study considers the effects of the appointments on the waiting time at the terminal when the appointment schedule is constructed. This paper introduces a mathematical formulation and a heuristic algorithm based on the Frank–Wolfe algorithm to solve the problem within a reasonable computational time. Numerical experiments are conducted to compare the proposed algorithm with the other heuristic approaches and analyze the effects of the appointments using empirical data. In addition, the impact of appointments by multiple trucking companies is examined.

Keywords

Container terminal Appointment system Trucking company Scheduling 

Notes

Funding

Funding was provided by National Research Foundation of Korea (2016R1D1A3B03934161).

References

  1. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows. Prentice Hall, New YorkzbMATHGoogle Scholar
  2. Bellman R (1958) On a routing problem. Q Appl Math 16:87–90CrossRefzbMATHGoogle Scholar
  3. Chen G, Govindan K, Yang Z (2013) Managing truck arrivals with time windows to alleviate gate congestion at container terminal. Int J Prod Econ 141(1):179–188CrossRefGoogle Scholar
  4. Dijkstra E (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271MathSciNetCrossRefzbMATHGoogle Scholar
  5. Duan F (1994) A faster algorithm for shortest path-SPFA. J Southwest Jiao Tong 2:207–212zbMATHGoogle Scholar
  6. Giuliano G, O’Brien T (2007) Reducing port-related truck emissions: the terminal gate appointment system at the ports of Los Angeles and Long Beach. Transp Res Part D 12(7):460–473CrossRefGoogle Scholar
  7. Heilig L, Lalla-Ruiz E, Voß S (2017a) Multi-objective inter-terminal truck routing. Transp Res Part E 106:178–202CrossRefGoogle Scholar
  8. Heilig L, Lalla-Ruiz E, Voß S (2017b) Port-IO: an integrative mobile cloud platform for real-time inter-terminal truck routing optimization. Flex Serv Manuf J 29(3–4):504–534CrossRefGoogle Scholar
  9. Horn RA, Johnson CR (2013) Matrix analysis. Cambridge University Press, New YorkzbMATHGoogle Scholar
  10. Kim HJ (2017) An operation method of truck appointment system utilizing estimated waiting time in container terminals. Pusan National University, Thesis PresentationGoogle Scholar
  11. Lalla-Ruiz E, Armas J, Expósito-Izquierdo C, Melián-Batista B, Moreno-Vega JM (2015) A multi-stage approach aimed at optimizing the transshipment of containers in a maritime container terminal. In: 15th international conference on computer aided systems theory, LNCS, vol 9520. Springer International Publishing, pp 255–262Google Scholar
  12. Murty KG, Wan YW, Liu J, Tseng MM, Leung E, Lai KK, Chiu HWC (2005) Hongkong international terminals gains elastic capacity using a data- intensive decision-support system. Interfaces 35:61–75CrossRefGoogle Scholar
  13. Nossack J, Pesch E (2013) A truck scheduling problem arising in intermodal container transportation. Eur J Oper Res 230:666–680MathSciNetCrossRefzbMATHGoogle Scholar
  14. Phan TMH, Kim KH (2015a) Negotiating truck arrival times among trucking companies. Transp Res Part E 75:132–144CrossRefGoogle Scholar
  15. Phan TMH, Kim KH (2015b) Truck appointment system for transshipment containers in terminals. In: Proceedings of the Asia Pacific industrial engineering and management systems conference 2015, pp 1560–1567Google Scholar
  16. Phan TMH, Kim KH (2016) Collaborative truck scheduling and appointments for trucking companies and container terminals. Transp Res Part B 86:37–50CrossRefGoogle Scholar
  17. Pusan East Container Terminal Co Ltd (2004) Shinsundae terminal stevedoring tariff. Pusan East Container Terminal Co Ltd, BusanGoogle Scholar
  18. Riaventin VN, Kim KH (2018) Scheduling appointments of truck arrivals at container terminals. Int J Ind Eng 25(5):590–603Google Scholar
  19. Schulte F, Lalla-Ruiz E, González-Ramírez RG, Voß S (2017) Reducing port-related empty truck emissions: a mathematical approach for truck appointments with collaboration. Transp Res Part E 105:195–212CrossRefGoogle Scholar
  20. Stanley R (2013) Algebraic combinatorics: walks, tree, tableaux, and more. Springer, New YorkCrossRefzbMATHGoogle Scholar
  21. Steenken D, Voss S, Stahlbock R (2004) Container terminal operation and operations research—a classification and literature review. OR Spectr 26:3–49CrossRefzbMATHGoogle Scholar
  22. Wang WF, Yun WY (2013) Scheduling for inland container truck and train transportation. Int J Prod Econ 143(2):349–356CrossRefGoogle Scholar
  23. Zehendner E, Feillet D (2014) Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal. Eur J Oper Res 235:461–469MathSciNetCrossRefzbMATHGoogle Scholar
  24. Zhang R, Yun WY, Moon IK (2009) A reactive tabu search algorithm for the multi-depot container truck transportation problem. Transp Res Part E 45:904–914CrossRefGoogle Scholar
  25. Zhang R, Yun WY, Kopfer H (2010) Heuristic-based truck scheduling for inland container transportation. OR Spectr 32(3):787–808CrossRefzbMATHGoogle Scholar
  26. Zhang X, Zeng Q, Chen W (2013) Optimization model for truck appointment in container terminals. Procedia Soc Behav Sci 96:1938–1947CrossRefGoogle Scholar
  27. Zhao W, Goodchild AV (2010) The impact of truck arrival information on container terminal rehandling. Transp Res Part E 46(3):327–343CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.International Graduate School (IGS) for Dynamics in Logistics, Production EngineeringUniversity of BremenBremenGermany
  2. 2.Department of Computer EngineeringPusan National UniversityBusanRepublic of Korea
  3. 3.Department of Industrial EngineeringPusan National UniversityBusanRepublic of Korea

Personalised recommendations