IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v11y2017i2p74-89.html
   My bibliography  Save this article

The Berth-Quay Cranes and Trucks Scheduling Optimization Problem by Hybrid Intelligence Swam Algorithm

Author

Listed:
  • Yi Liu

    (Hangzhou Dianzi University, Management School, Hangzhou, China)

  • Sabina Shahbazzade

    (University of California, Electrical Engineering and Computer Sciences, Berkeley, CA, USA)

Abstract

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. The proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.

Suggested Citation

  • Yi Liu & Sabina Shahbazzade, 2017. "The Berth-Quay Cranes and Trucks Scheduling Optimization Problem by Hybrid Intelligence Swam Algorithm," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 11(2), pages 74-89, April.
    • Handle: RePEc:igg:jcini0:v:11:y:2017:i:2:p:74-89
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2017040105
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jcini0:v:11:y:2017:i:2:p:74-89. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.