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Solving the container relocation problem by an improved greedy look-ahead heuristic

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  • Jin, Bo
  • Zhu, Wenbin
  • Lim, Andrew

Abstract

The container relocation problem (CRP) is one of the most crucial issues for container terminals. In a single bay, containers belonging to multiple groups should be retrieved by an equipped yard crane in accordance with their retrieval priorities. An operation of the crane can either relocate a container from the top of a stack to another within the bay, or remove a container with the highest retrieval priority among all remaining containers. The objective of the CRP is to find an optimized operation plan for the crane with the fewest number of container relocations. This paper proposes an improved greedy look-ahead heuristic for the CRP and conducts experiments on four existing data sets. The experimental results show that the proposed approach is able to provide better solutions for large-scale instances in shorter runtime, compared to the up-to-date approaches in the recent literature.

Suggested Citation

  • Jin, Bo & Zhu, Wenbin & Lim, Andrew, 2015. "Solving the container relocation problem by an improved greedy look-ahead heuristic," European Journal of Operational Research, Elsevier, vol. 240(3), pages 837-847.
    • Handle: RePEc:eee:ejores:v:240:y:2015:i:3:p:837-847
    DOI: 10.1016/j.ejor.2014.07.038
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    References listed on IDEAS

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    Cited by:

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    3. Galle, Virgile & Barnhart, Cynthia & Jaillet, Patrick, 2018. "A new binary formulation of the restricted Container Relocation Problem based on a binary encoding of configurations," European Journal of Operational Research, Elsevier, vol. 267(2), pages 467-477.
    4. Ting, Ching-Jung & Wu, Kun-Chih, 2017. "Optimizing container relocation operations at container yards with beam search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 17-31.
    5. Wang, Ning & Jin, Bo & Lim, Andrew, 2015. "Target-guided algorithms for the container pre-marshalling problem," Omega, Elsevier, vol. 53(C), pages 67-77.
    6. Ku, Dusan & Arthanari, Tiru S., 2016. "Container relocation problem with time windows for container departure," European Journal of Operational Research, Elsevier, vol. 252(3), pages 1031-1039.
    7. Gharehgozli, Amir Hossein & Vernooij, Floris Gerardus & Zaerpour, Nima, 2017. "A simulation study of the performance of twin automated stacking cranes at a seaport container terminal," European Journal of Operational Research, Elsevier, vol. 261(1), pages 108-128.
    8. Tanaka, Shunji & Voß, Stefan, 2019. "An exact algorithm for the block relocation problem with a stowage plan," European Journal of Operational Research, Elsevier, vol. 279(3), pages 767-781.
    9. Wang, Ning & Jin, Bo & Zhang, Zizhen & Lim, Andrew, 2017. "A feasibility-based heuristic for the container pre-marshalling problem," European Journal of Operational Research, Elsevier, vol. 256(1), pages 90-101.
    10. Andresson Silva Firmino & Ricardo Martins Abreu Silva & Valéria Cesário Times, 2019. "A reactive GRASP metaheuristic for the container retrieval problem to reduce crane’s working time," Journal of Heuristics, Springer, vol. 25(2), pages 141-173, April.
    11. Zehendner, Elisabeth & Caserta, Marco & Feillet, Dominique & Schwarze, Silvia & Voß, Stefan, 2015. "An improved mathematical formulation for the blocks relocation problem," European Journal of Operational Research, Elsevier, vol. 245(2), pages 415-422.
    12. Huiling Zhu, 2022. "Integrated Containership Stowage Planning: A Methodology for Coordinating Containership Stowage Plan and Terminal Yard Operations," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    13. Boge, Sven & Goerigk, Marc & Knust, Sigrid, 2020. "Robust optimization for premarshalling with uncertain priority classes," European Journal of Operational Research, Elsevier, vol. 287(1), pages 191-210.
    14. Kap Hwan Kim & Sanghyuk Yi, 2021. "Utilizing information sources to reduce relocation of inbound containers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(4), pages 726-749, December.
    15. Gharehgozli, Amir & Zaerpour, Nima, 2018. "Stacking outbound barge containers in an automated deep-sea terminal," European Journal of Operational Research, Elsevier, vol. 267(3), pages 977-995.
    16. Gharehgozli, Amir & Yu, Yugang & de Koster, René & Du, Shaofu, 2019. "Sequencing storage and retrieval requests in a container block with multiple open locations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 261-284.
    17. Jovanovic, Raka & Tuba, Milan & Voß, Stefan, 2019. "An efficient ant colony optimization algorithm for the blocks relocation problem," European Journal of Operational Research, Elsevier, vol. 274(1), pages 78-90.
    18. Feng, Yuanjun & Song, Dong-Ping & Li, Dong & Xie, Ying, 2022. "Service fairness and value of customer information for the stochastic container relocation problem under flexible service policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    19. Huiling Zhu & Mingjun Ji & Wenwen Guo & Qingbin Wang & Yongzhi Yang, 2019. "Mathematical formulation and heuristic algorithm for the block relocation and loading problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(4), pages 333-351, June.
    20. Zhang, Canrong & Guan, Hao & Yuan, Yifei & Chen, Weiwei & Wu, Tao, 2020. "Machine learning-driven algorithms for the container relocation problem," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 102-131.

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