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Variable neighborhood search for minimum cost berth allocation

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  • Hansen, Pierre
  • Oguz, Ceyda
  • Mladenovic, Nenad
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    Abstract

    The berth allocation problem is to allocate space along the quayside to incoming ships at a container terminal in order to minimize some objective function. We consider minimization of total costs for waiting and handling as well as earliness or tardiness of completion, for all ships. We assume ships can arrive at any given time, i.e., before or after the berths become available. The resulting problem, which subsumes several previous ones, is expressed as a linear mixed 0-1 program. As it turns out to be too time-consuming for exact solution of instances of realistic size, a Variable Neighborhood Search (VNS) heuristic is proposed, and compared with Multi-Start (MS), a Genetic Search algorithm (GA) and a Memetic Search algorithm (MA). VNS provides optimal solutions for all instances solved to optimality in a previous paper of the first two authors and outperforms MS, MA and GA on large instances.

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    File URL: http://www.sciencedirect.com/science/article/B6VCT-4N2D2T8-2/1/79993219ed418653fb42c9cffe4fd112
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 191 (2008)
    Issue (Month): 3 (December)
    Pages: 636-649

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    Handle: RePEc:eee:ejores:v:191:y:2008:i:3:p:636-649

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    Web page: http://www.elsevier.com/locate/eor

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    References

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    1. Kim, Kap Hwan & Moon, Kyung Chan, 2003. "Berth scheduling by simulated annealing," Transportation Research Part B: Methodological, Elsevier, vol. 37(6), pages 541-560, July.
    2. Legato, Pasquale & Mazza, Rina M., 2001. "Berth planning and resources optimisation at a container terminal via discrete event simulation," European Journal of Operational Research, Elsevier, vol. 133(3), pages 537-547, September.
    3. Imai, Akio & Nishimura, Etsuko & Papadimitriou, Stratos, 2003. "Berth allocation with service priority," Transportation Research Part B: Methodological, Elsevier, vol. 37(5), pages 437-457, June.
    4. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    5. Vis, Iris F. A. & de Koster, Rene, 2003. "Transshipment of containers at a container terminal: An overview," European Journal of Operational Research, Elsevier, vol. 147(1), pages 1-16, May.
    6. Imai, Akio & Nishimura, Etsuko & Papadimitriou, Stratos, 2001. "The dynamic berth allocation problem for a container port," Transportation Research Part B: Methodological, Elsevier, vol. 35(4), pages 401-417, May.
    7. Nishimura, Etsuko & Imai, Akio & Papadimitriou, Stratos, 2001. "Berth allocation planning in the public berth system by genetic algorithms," European Journal of Operational Research, Elsevier, vol. 131(2), pages 282-292, June.
    8. Imai, Akio & Sun, Xin & Nishimura, Etsuko & Papadimitriou, Stratos, 2005. "Berth allocation in a container port: using a continuous location space approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 199-221, March.
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    Cited by:
    1. Zhen, Lu & Lee, Loo Hay & Chew, Ek Peng, 2011. "A decision model for berth allocation under uncertainty," European Journal of Operational Research, Elsevier, vol. 212(1), pages 54-68, July.
    2. Xu, Dongsheng & Li, Chung-Lun & Leung, Joseph Y.-T., 2012. "Berth allocation with time-dependent physical limitations on vessels," European Journal of Operational Research, Elsevier, vol. 216(1), pages 47-56.

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