Crane Intensity and Block Stowage Strategies in Stowage Planning

  • Dario PacinoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11184)


The increasing size of container vessels is raising the complexity of daily operations of both the carrier and the terminal. This paper focuses on stowage planning, the problem of assigning container to positions in a vessel. In particular, it studies the implementation of known planning strategies within an optimisation framework. Block stowage and crane intensity are presented and mathematically modelled on a simplified version of the problem. An experimental evaluation, on a large set of novel benchmark instances, shows that even in this simplified version the problem is not trivially solved. A matheuristic based on large neighbourhood search is presented, which is able to find a solution to all instances in short computational times.



The author would like to thank the Danish Maritime Foundation for supporting this research under the project 2015-119 DTU Transport, Dynastow. Thanks are also due to Roberto Roberti for the fruitful discussions about the mathematical formulations.


  1. 1.
    Ambrosino, D., Paolucci, M., Sciomachen, A.: A MIP heuristic for multi port stowage planning. Transp. Res. Procedia 10, 725–734 (2015)CrossRefGoogle Scholar
  2. 2.
    Ambrosino, D., Paolucci, M., Sciomachen, A.: Computational evaluation of a MIP model for multi-port stowage planning problems. Soft Comput. 21(7), 1753–1763 (2017)CrossRefGoogle Scholar
  3. 3.
    Avriel, M., Penn, M., Shpirer, N., Witteboon, S.: Stowage planning for container ships to reduce the number of shifts. Ann. Oper. Res. 76, 55–71 (1998)CrossRefGoogle Scholar
  4. 4.
    Christensen, J., Pacino, D.: A matheuristic for the Cargo Mix Problem with Block Stowage. Transp. Res. Part E Logist. Transp. Rev. 97, 151–171 (2017)CrossRefGoogle Scholar
  5. 5.
    Ding, D., Chou, M.C.: Stowage planning for container ships: a heuristic algorithm to reduce the number of shifts. Eur. J. Oper. Res. 246(1), 242–249 (2015)CrossRefGoogle Scholar
  6. 6.
    Pacino, D., Delgado, A., Jensen, R.M., Bebbington, T.: Fast generation of near-optimal plans for eco-efficient stowage of large container vessels. In: Böse, J.W., Hu, H., Jahn, C., Shi, X., Stahlbock, R., Voß, S. (eds.) ICCL 2011. LNCS, vol. 6971, pp. 286–301. Springer, Heidelberg (2011). Scholar
  7. 7.
    Pacino, D., Delgado, A., Jensen, R.M., Bebbington, T.: An accurate model for seaworthy container vessel stowage planning with ballast tanks. In: Hu, H., Shi, X., Stahlbock, R., Voß, S. (eds.) ICCL 2012. LNCS, vol. 7555, pp. 17–32. Springer, Heidelberg (2012). Scholar
  8. 8.
    Parreño, F., Pacino, D., Alvarez-Valdes, R.: A GRASP algorithm for the container stowage slot planning problem. Transp. Res. Part E Logist. Transp. Rev. 94, 141–157 (2016)CrossRefGoogle Scholar
  9. 9.
    Pisinger, D., Ropke, S.: Large neighborhood search. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, pp. 399–419. Springer, Boston (2010). Scholar
  10. 10.
    Roberti, R., Pacino, D.: A decomposition method for finding optimal container stowage plans (2018, accepted manuscript to appear in Transportation Science)CrossRefGoogle Scholar
  11. 11.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998). Scholar
  12. 12.
    Tierney, K., Pacino, D., Jensen, R.M.: On the complexity of container stowage planning problems. Discrete Appl. Math. 169, 225–230 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    UNCTAD: Review of maritime transport 2017 (2017)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.DTU Management EngineeringTechnical University of DenmarkKongens LyngbyDenmark

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