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Models and algorithms for multi-crane oriented scheduling method in container terminals

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  • Zeng, Qingcheng
  • Yang, Zhongzhen
  • Lai, Luyuan

Abstract

"Multi-crane oriented" is a scheduling method that yard trailers can be shared by different quay cranes. In this paper, two models for this problem are developed. The first one is a model for an inter-ship-based sharing method. In this model, yard trailers can be shared by quay cranes of different ships. To solve the model, a two-phase Tabu search algorithm is designed. The second one is a model for a ship-based sharing method. In this model, yard trailers can only be shared by quay cranes of the same ship. Q-learning algorithm is designed to solve the model. Numerical tests show that the "multi-crane oriented" method can decrease the yard trailers' travel distance, reduce the disequilibrium of different working lines, and thus improve the operation efficiency in container terminals.

Suggested Citation

  • Zeng, Qingcheng & Yang, Zhongzhen & Lai, Luyuan, 2009. "Models and algorithms for multi-crane oriented scheduling method in container terminals," Transport Policy, Elsevier, vol. 16(5), pages 271-278, September.
    • Handle: RePEc:eee:trapol:v:16:y:2009:i:5:p:271-278
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    References listed on IDEAS

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    Cited by:

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    2. Fotuhi, Fateme & Huynh, Nathan & Vidal, Jose M. & Xie, Yuanchang, 2013. "Modeling yard crane operators as reinforcement learning agents," Research in Transportation Economics, Elsevier, vol. 42(1), pages 3-12.
    3. Schwientek, Anne Kathrina & Lange, Ann-Kathrin & Jahn, Carlos, 2017. "Literature classification on dispatching of container terminal vehicles," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digitalization in Maritime and Sustainable Logistics: City Logistics, Port Logistics and Sustainable Supply Chain Management in the Digital Age. Proce, volume 24, pages 3-36, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Andreas Kuhnle & Jan-Philipp Kaiser & Felix Theiß & Nicole Stricker & Gisela Lanza, 2021. "Designing an adaptive production control system using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 855-876, March.

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