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A Hybrid Hyperheuristic Approach for the Containership Stowage Problem Considering the Ship Stability

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Advances in Computational Intelligence (MICAI 2022)

Abstract

In this work we face the Containership Stowage Problem, also referred to as Master Bay Planning Problem (MBPP) in the literature. MBPP is an NP-hard combinatorial optimization problem that consists in finding the best plan to load a set of containers into a set of available locations in the containership, subject to several structural and operational constraints. This problem is really difficult to solve and very import in the context of maritime port logistics. Since it is a practical decision-making problem with high complexity and challenging instances, a hybrid hierarchical approach is developed in this paper. Our algorithmic proposal couples a heuristic procedure with a perturbative hyperheuristic. The validation of the proposed approach is performed by solving pseudo-randomly instances available in the literature. The computational results show the efficiency of the proposed hybrid hierarchical algorithm when comparing with the reference results from the literature.

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Acknowledgments

The authors would like to thank Tecnológico Nacional de México for their support in this research. The authors thank the Mexican Council for Science and Technology (CONACYT) for its support through the Mexican National System of Researchers (SNI). Besides, the first author also thanks the CONACYT postdoctoral program for the grant to develop this research.

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Correspondence to Paula Hernández-Hernández .

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Hernández-Hernández, P., Cruz-Reyes, L., Melin, P., Castillo-García, N., Gómez-Santillán, C.G. (2022). A Hybrid Hyperheuristic Approach for the Containership Stowage Problem Considering the Ship Stability. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13612. Springer, Cham. https://doi.org/10.1007/978-3-031-19493-1_33

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  • DOI: https://doi.org/10.1007/978-3-031-19493-1_33

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