References
- Abdelali, C., Khadidja, Y., & Bellatreche, L. (2023) Optimization of dangerous goods stowage : A hybrid approach between faster R-CNN and coronavirus metaheuristic. In: 2023 IEEE International Conference on Big Data (BigData), 2561‑2569. https://doi.org/10.1109/BigData59044.2023.10386793.
- Amrani, F., Yachba, K., Belayachi, N., & Hamdadou, D. (2018) A decision-making system for container storage management in a seaport using the ant-colony optimisation algorithm. International Journal of Management and Decision Making, 17(3), 348-367.
- Azab, A., & Morita, H. (2022a) A zero-blockage based scheduling for import containers pickup operations at container terminal yards. In: Proceedings of the 11th International Conference on Operations Research and Enterprise Systems, 286‑293. https://doi.org/10.5220/0010885000003117.
- Azab, A., & Morita, H. (2022b) The block relocation problem with appointment scheduling. European Journal of Operational Research, 297(2), 680‑694. https://doi.org/10.1016/j.ejor.2021.06.007.
- Bacci, T., Mattia, S., & Ventura, P. (2019) The bounded beam search algorithm for the block relocation problem. Computers & Operations Research, 103, 252‑264. https://doi.org/10.1016/j.cor.2018.11.008.
- Bacci, T., Mattia, S., & Ventura, P. (2023) The realization-independent reallocation heuristic for the stochastic container relocation problem. Soft Computing, 27(7), 4223‑4233. https://doi.org/10.1007/s00500-022-07070-3.
- Belayachi, N., Gelareh, S., Yachba, K., & Bouamrane, K. (2017) The logistic of empty containers′ return in the liner-shipping network. Transport and Telecommunication Journal, 18(3), 207-219.
- Bendaoud, Z., & Yachba, K. (2017) Towards a decision support system for optimization of container placement in a container terminal. International Journal of Strategic Information Technology and Applications (IJSITA), 8(3), 59-72.
- Caserta, M., Schwarze, S., & Voß, S. (2012). A mathematical formulation and complexity considerations for the blocks relocation problem. European Journal of Operational Research, 219(1), 96‑104. https://doi.org/10.1016/j.ejor.2011.12.039
- Chaslot, G., Winands, M.H.M., Jaap Van Den Herik, H., Uiterwijk, J. W. H. M., Bouzy, B. (2008) Progressive Strategies for Monte-Carlo Tree Search. New Mathematics and Natural Computation, 04(03), 343‑57. doi: 10.1142/S1793005708001094.
- Coquelin, P.-A., Munos, R. (2007) Bandit algorithms for tree search. https://doi.org/10.48550/ARXIV.CS/0703062.
- Da Silva Firmino, A., & Cesário Times, V. (2024) A hybrid biased random-key genetic algorithm for the container relocation problem. In M. Khosravy, N. Gupta, & O. Witkowski (Éds.), Frontiers in Genetics Algorithm Theory and Applications, 55‑80. Springer Nature Singapore. https://doi.org/10.1007/978-981-99-8107-6_4.
- Đurasević, M., & Đumić, M. (2024) Designing relocation rules with genetic programming for the container relocation problem with multiple bays and container groups. Applied Soft Computing, 150, 111104. https://doi.org/10.1016/j.asoc.2023.111104
- Đurasević, M., Đumić, M., Čorić, R., & Gil-Gala, F. J. (2023). Automated design of relocation rules for minimising energy consumption in the container relocation problem (arXiv:2307.01513). arXiv. http://arxiv.org/abs/2307.01513
- Fabbri, A. (2015) Dynamique d’apprentissage pour Monte Carlo Tree Search : Applications aux jeux de Go et du Clobber solitaire impartial. Université Claude Bernard - Lyon 1, 214.
- Feng, Y., Song, D.-P., Li, D., & Zeng, Q. (2020) The stochastic container relocation problem with flexible service policies. Transportation Research Part B: Methodological, 141, 116‑163. https://doi.org/10.1016/j.trb.2020.09.006.
- Galle, V., Barnhart, C., & Jaillet, P. (2018) A new binary formulation of the restricted Container Relocation Problem based on a binary encoding of configurations. European Journal of Operational Research, 267(2), 467‑477. https://doi.org/10.1016/j.ejor.2017.11.053.
- Galle, V., Manshadi, V. H., Boroujeni, S. B., Barnhart, C., & Jaillet, P. (2018) The Stochastic Container Relocation Problem. Transportation Science, 52(5), 1035‑1058. https://doi.org/10.1287/trsc.2018.0828.
- Gulić, M., Maglić, L., Krljan, T., Maglić, L. (2022) Solving the container relocation problem by using a metaheuristic genetic algorithm. Applied Sciences, 12(15), 7397. doi:10.3390/app12157397.
- Jiang, T., Zeng, B., Wang, Y., & Yan, W. (2021) A new heuristic reinforcement learning for container relocation problem. Journal of Physics: Conference Series, 1873(1), 012050. https://doi.org/10.1088/1742-6596/1873/1/012050.
- Jin, B. (2020) On the integer programming formulation for the relaxed restricted container relocation problem. European Journal of Operational Research, 281(2), 475‑482. https://doi.org/10.1016/j.ejor.2019.08.041.
- Jovanovic, R., Tanaka, S., Nishi, T., & Voß, S. (2019) A GRASP approach for solving the Blocks Relocation Problem with Stowage Plan. Flexible Services and Manufacturing Journal, 31(3), 702‑729. https://doi.org/10.1007/s10696-018-9320-3.
- Jovanovic, R., & Voß, S. (2014). A chain heuristic for the Blocks Relocation Problem. Computers & Industrial Engineering, 75, 79‑86. https://doi.org/10.1016/j.cie.2014.06.010.
- Khadidja, Y., Amina, F. I., & Eddine, B. S. (2024). Enhancing Transportation Efficiency with Optimal Container Placement Using the Bat Algorithm. Transport and Telecommunication Journal, 25(2), 200-208.
- Kim, K. H., & Hong, G.-P. (2006). A heuristic rule for relocating blocks. Computers & Operations Research, 33(4), 940‑954. https://doi.org/10.1016/j.cor.2004.08.005.
- Kimms, A., & Wilschewski, F. (2023) A new modeling approach for the unrestricted block relocation problem. OR Spectrum, 45(4), 1071‑1111. https://doi.org/10.1007/s00291-023-00728-w.
- Kocsis, L., & Szepesvári, C. (2006) Bandit Based Monte-Carlo Planning. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Éds.), Machine Learning: ECML 2006, 4212, 282‑293. Springer Berlin Heidelberg. https://doi.org/10.1007/11871842_29.
- Liu, F., Ye, T., & Zhang, Z. (2023) Dynamic Attention Model – A Deep Reinforcement Learning Approach for Container Relocation Problem. In H. Fujita, Y. Wang, Y. Xiao, & A. Moonis (Éds.), Advances and Trends in Artificial Intelligence. Theory and Applications, 13926, 273‑285. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36822-6_24.
- Maglić, L., Gulić, M., & Maglić, L. (2019) Optimization of Container Relocation Operations in Port Container Terminals. Transport, 35(1), 37‑47. https://doi.org/10.3846/transport.2019.11628.
- Ozcan, S., & Eliiyi, D. T. (2017) A reward-based algorithm for the stacking of outbound containers. Transportation Research Procedia, 22, 213‑221. https://doi.org/10.1016/j.trpro.2017.03.028.
- Perez, D., Mostaghim, S., Samothrakis, S., & Lucas, S. M. (2015) Multiobjective Monte Carlo Tree Search for Real-Time Games. IEEE Transactions on Computational Intelligence and AI in Games, 7(4), 347‑360. https://doi.org/10.1109/TCIAIG.2014.2345842
- Sutton, R. S., & Barto, A. G. (2018) Reinforcement learning: An introduction (Second edition). The MIT Press.
- Tahiri, H., Khadidja, Y., & Bouamrane, K. (2022) Towards a multi-criteria decision approach to solving the container storage problem: container ship loading component. International Journal of Organizational and Collective Intelligence (IJOCI), 12(1), 1-21.
- Tahiri, H., Yachba, K., & Bouamrane, K. (2020) A container placement approach in a container-ship based on the electre 2 method. In: 4th International Symposium on Informatics and its Applications (ISIA), IEEE, 1-5.
- Tanaka, S., & Voß, S. (2019). An exact algorithm for the block relocation problem with a stowage plan. European Journal of Operational Research, 279(3), 767‑781. https://doi.org/10.1016/j.ejor.2019.06.014.
- Wang, Z., Chenhao, Zh., Ada, Ch., Jingkun, G. (2024) A policy-based Monte Carlo Tree Search method for container pre-marshalling. International Journal of Production Research, 62(13), 4776‑92. doi:10.1080/00207543.2023.2279130.
- Wei, L., Wei, F., Schmitz, S., Kunal, K., Noche, B. (2021) Optimization of container relocation problem via reinforcement learning. Logistics Journal: Proceedings – ISSN 2192-9084, 17. doi: 10.2195/lj_Proc_wei_en_202112_02.
- Yachba, K., Bendaoud, Z., & Bouamrane, K. (2018) A technique for resolution of the assignment problem containers in a container terminal. In: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, IGI Global, 120-132.
- Yachba, K., Bendaoud, Z., & Bouamrane, K. (2018) Toward a decision support system for regulation in an urban transport network. International Journal of Strategic Information Technology and Applications (IJSITA), 9(2), 1-17.
- Yachba, K., Bouamrane, K., & Gelareh, S. (2015) Containers storage optimization in a container terminal using a multimethod multi-level approach. In: The International Conference on Computers & Industrial Engineering (CIE45), 28-30.
- Yachba, K., Chaabane, A., & Benadda, M.A. (2021) Un système d’aide à la décision pour l’optimisation de processus de distribution des produits finaux. In : Séminaire international sur les mathématiques et l’informatique, Février, Oran.
- Yachba, K., Gelareh, S., & Bouamrane, K. (2016) Storage management of hazardous containers using the genetic algorithm. Transport and Telecommunication Journal, 17(4), 371-383.
- Ye, R., Ye, R., & Zheng, S. (2023) Machine Learning Guides the Solution of Blocks Relocation Problem in Container Terminals. Transportation Research Record: Journal of the Transportation Research Board, 2677(3), 721‑737. https://doi.org/10.1177/03611981221117157.
- Zhang, C., Guan, H., Yuan, Y., Chen, W., & Wu, T. (2020) Machine learning-driven algorithms for the container relocation problem. Transportation Research Part B: Methodological, 139, 102‑131. https://doi.org/10.1016/j.trb.2020.05.017.
- Zhang, Zh., Tan, K.Ch., Qin, W., Li, Y., Ek Peng Chew, E.P., Xu, K. (2023) Online container relocation problem with retrieval probability. doi:10.2139/ssrn.4604944.
- Zhou, S., & Zhang, Q. (2024) Real-time batch optimization for the stochastic container relocation problem. Applied Sciences, 14(6), 2624. https://doi.org/10.3390/app14062624.
- Zhu, W., Qin, H., Lim, A., & Zhang, H. (2012) Iterative deepening A* algorithms for the container relocation problem. IEEE Transactions on Automation Science and Engineering, 9(4), 710‑722. https://doi.org/10.1109/TASE.2012.2198642.
- Zweers, B. G., Bhulai, S., & Van Der Mei, R. D. (2020) Optimizing pre-processing and relocation moves in the stochastic container relocation problem. European Journal of Operational Research, 283(3), 954‑971. https://doi.org/10.1016/j.ejor.2019.11.067.