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An efficient ant colony optimization algorithm for the blocks relocation problem

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  • Jovanovic, Raka
  • Tuba, Milan
  • Voß, Stefan

Abstract

In this paper we present an ant colony optimization (ACO) algorithm for the Blocks Relocation Problem (BRP). The method is applied to both versions of the problem most commonly considered in literature, i.e., the restricted (rBRP) and the unrestricted (uBRP) BRP with distinct due dates. In case of the uBRP a new heuristic is proposed and incorporated in a standard greedy algorithm. The performance of the basic greedy approach is enhanced by extending it to the ACO metaheuristic. In it, a novel approach for defining the pheromone matrix is proposed. More precisely, it only stores a small amount of information instead of the complete bay state. Further, we show that the proposed ACO method can easily be adapted for solving the BRP in which the objective function is related to the crane operation time. Our computational results show that the proposed approach manages to outperform existing methods for the BRP.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:274:y:2019:i:1:p:78-90
    DOI: 10.1016/j.ejor.2018.09.038
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    Cited by:

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    3. Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Ruiz, Rubén & Tierney, Kevin, 2020. "Minimizing crane times in pre-marshalling problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    4. 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.
    5. Azab, Ahmed & Morita, Hiroshi, 2022. "The block relocation problem with appointment scheduling," European Journal of Operational Research, Elsevier, vol. 297(2), pages 680-694.
    6. 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.
    7. Zweers, Bernard G. & Bhulai, Sandjai & van der Mei, Rob D., 2020. "Optimizing pre-processing and relocation moves in the Stochastic Container Relocation Problem," European Journal of Operational Research, Elsevier, vol. 283(3), pages 954-971.
    8. Wei Song & Shuailei Yuan & Yun Yang & Chufeng He, 2022. "A Study of Community Group Purchasing Vehicle Routing Problems Considering Service Time Windows," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    9. 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.
    10. Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Parreño, Francisco, 2022. "A beam search algorithm for minimizing crane times in premarshalling problems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1063-1078.
    11. 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.
    12. 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.
    13. Morin, Michael & Abi-Zeid, Irène & Quimper, Claude-Guy, 2023. "Ant colony optimization for path planning in search and rescue operations," European Journal of Operational Research, Elsevier, vol. 305(1), pages 53-63.

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