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A branch-and-cut algorithm for the restricted Block Relocation Problem

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  • Bacci, Tiziano
  • Mattia, Sara
  • Ventura, Paolo

Abstract

We consider the Block Relocation Problem, that has a crucial role in the logistics of containers. It consists of minimizing the number of container relocations within a container bay/yard. Since the number of containers shipped worldwide grew dramatically in the last years, the problem has been widely investigated. Here we propose an exact algorithm for the restricted version of the Block Relocation Problem, based on a new integer linear programming formulation. We compare such new approach with the state-of-art exact methods. The computational results prove its effectiveness and show that it outperforms all the previously proposed procedures, in almost all the considered instances.

Suggested Citation

  • Bacci, Tiziano & Mattia, Sara & Ventura, Paolo, 2020. "A branch-and-cut algorithm for the restricted Block Relocation Problem," European Journal of Operational Research, Elsevier, vol. 287(2), pages 452-459.
    • Handle: RePEc:eee:ejores:v:287:y:2020:i:2:p:452-459
    DOI: 10.1016/j.ejor.2020.05.029
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    References listed on IDEAS

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    1. Yat‐wah Wan & Jiyin Liu & Pei‐Chun Tsai, 2009. "The assignment of storage locations to containers for a container stack," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(8), pages 699-713, December.
    2. 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.
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    4. Caserta, Marco & Schwarze, Silvia & Voß, Stefan, 2012. "A mathematical formulation and complexity considerations for the blocks relocation problem," European Journal of Operational Research, Elsevier, vol. 219(1), pages 96-104.
    5. 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.
    6. de Melo da Silva, Marcos & Toulouse, Sophie & Wolfler Calvo, Roberto, 2018. "A new effective unified model for solving the Pre-marshalling and Block Relocation Problems," European Journal of Operational Research, Elsevier, vol. 271(1), pages 40-56.
    7. 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.
    8. Lixin Tang & Wei Jiang & Jiyin Liu & Yun Dong, 2015. "Research into container reshuffling and stacking problems in container terminal yards," IISE Transactions, Taylor & Francis Journals, vol. 47(7), pages 751-766, July.
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    Cited by:

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    2. Jiménez-Piqueras, Celia & Ruiz, Rubén & Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon, 2023. "A constraint programming approach for the premarshalling problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 668-678.
    3. Azab, Ahmed & Morita, Hiroshi, 2022. "The block relocation problem with appointment scheduling," European Journal of Operational Research, Elsevier, vol. 297(2), pages 680-694.
    4. Feng, Yuanjun & Song, Dong-Ping & Li, Dong, 2022. "Smart stacking for import containers using customer information at automated container terminals," European Journal of Operational Research, Elsevier, vol. 301(2), pages 502-522.
    5. 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).
    6. Tanaka, Shunji & Voß, Stefan, 2022. "An exact approach to the restricted block relocation problem based on a new integer programming formulation," European Journal of Operational Research, Elsevier, vol. 296(2), pages 485-503.
    7. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.

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