Total views : 335

An Analysis on the Modeling of Container Terminal Operations


  • Department of Harbour and Ocean Engineering, AMET University, 135, East Coast Road, Kanathur, Chennai - 603112, Tamil Nadu, India


Objectives: Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. The present analysis describes these problems within the scope of container terminal modeling. Methods/Statistical Analysis: Basic formulation of the problem is stated as two-machine flow shop problem. The well-known maximum travelling salesman problem (Max TSP) has been applied in this study. Max TSP can be solved as a TSP by replacing each edge cost by its additive inverse, since, there is a different value for unloading stack i while loading stack j and loading stack i while unloading stack j; this model corresponds to the Asymmetric Travelling Salesman Problem (ATSP). Findings: It is found that, there is an essential need for improvements and optimization of all aspects in the container transportation chains. For the real life systems of this type, this problem has been solved optimally. Significant possibilities for time savings have been arrived in this study. For real life case, where the reloading is performed on two barges placed side by side (8 stacks in the bay, 11 bays, 4 containers in the stack) time savings as a function of the terminal length are presented. The time saving with respect to number of rows inside the container yard is presented. Simulation models of container cranes demonstrate significant time savings, if double cycling is applied. It is showed that application of the double cycling can result in time savings of 12 to 27 % depending on the system parameters. This analysis is based on discrete event simulation and analytical optimization methods. Application/Improvement: Good planning of container terminal operations reduces waiting time for liner ships. Reducing the waiting time increases customer satisfaction and improves the terminal productivity which gives the container terminal an advantage over its competitors.


Container Terminals, Modeling, Mathematical Models, Optimization, Scheduling, Simulations.

Full Text:

 |  (PDF views: 478)


  • Vacca I, Salani M, Bierlaire M. Optimization of operations in container terminals: Hierarchicalvs integrated approaches. Proceedings of the Swiss Transport Research Conference, STRC; Zurich, Switzerland. 2010.
  • Tu-Chang K. Development of a container terminal simulation model and its application in and analysis of terminal 18, port of Seattle [PhD thesis]. University of Washington; 1992.
  • Henesey L. Multi-agent container terminal management [PhD thesis]. Karlshamn: Blekinge Institute of Technology; 2006.
  • Kim KH, Günther HO. Container terminals and cargo systems. Springer-Verlag Berlin Heidelberg; 2007.
  • Steenken D, Voß S, Stahlbock R. Container terminal operation and operations research- A classification and literature review. OR Spectrum. 2004; (26):3–49.
  • Vis IFA, Koster R. Transshipment of containers at the container terminal: An overview. European Journal of Operational Research. 2003; (147):1–16.
  • Stahlbock R, Voß S. Operational research at container terminals: A literature update. OR Spectrum. 2008; (30):1–52.
  • Kim KH, Park YM. A crane scheduling method for port container terminals. European Journal of Operational Research. 2004; 156(3):752–68.
  • Murty KG. Yard crane pools and optimum layouts for storage yards of container terminals. Journal of Industrial and Systems Engineering. 2007; 1(3):190–9.
  • Günther HO, Kim KH. Container terminals and automated transport systems. New York: Springer, Berlin Heidelberg; 2005.
  • Crainic TG, Kim KH. Intermodal transportation. In: Barnhart C, Laporte G, editor. Handbook in OR & MS. Amsterdam: Elsevier. 2007; 14:467–537.
  • Kanniga E, Srikanth SMK, Sundhararajan. Optimization solution of equal dimension boxes in container loading problem using a permutation block algorithm. Indian Journal of Science and Technology. 2014 Jun; 7(S5):22–6.
  • Georgijevic M, Bojanic V, Bojanic G. Modelling and application challenges in the container terminal Operations. Available from:
  • Pesenti R, Ukovich W, Castelli L, Longo G. Quantitative models and methods for locating logistics nodes in maritime transportation. Quaderni di TrasportiEuropei: Study on Location of Logistic Nodes. 1998; (8-9):47–58.
  • Gudelj, Krčum M, Twrdy E. Models and methods for operations in port container Terminals. Promet- Traffic and Transportation. 2010; 22(1):43–51.
  • Meisel F, Bierwirth C. A unified approach for the evaluation of quay crane scheduling models and algorithms.Computers and operational Research. 2011; 38:683–93.
  • Goodchild AV, Daganzo CF. Double-cycling strategies for container ships and their effect on ship loading and unloading operations. Transportation Science. 2006; (40):473–83.
  • Zhang H, Kim KH. Maximizing the number of dual-cycle operations of quay cranes in container terminals. Computers and Industrial Engineering. 2009; (56):979–92.
  • Gutin G, Punnen PA. The traveling salesman problem and its variations. Kluwer Academic Publishers Dordrecht; 2002.
  • Pap E, Bojanic V, Bojanic G, Georgijevic M. Optimization of container quay cranes operations. 9th IEEE International Symposium on Intelligent Systems and Informatics SISY; Subotica, Serbia. 2011.
  • Sacone S, Siri S. An integrated simulation-optimization framework for the operational planning of seaport container terminal. Mathematical and Computer Modeling of Dynamic Systems. 2009; 15(3):275–93.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.