Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Container terminals serve as crucial hubs in the global supply chain, facilitating the efficient transfer of goods between different modes of transportation. This study explores optimization strategies for container terminal operations, focusing on the comparison between hierarchical and integrated solution approaches. A comprehensive literature review provides insights into the challenges and advancements in container terminal management. The comparative analysis highlights the advantages of integrated optimization models, particularly through the lens of the Tactical Berth Allocation Problem (TBAP). By incorporating real-world data and advanced computational methods, the study offers nuanced insights into efficiency and time estimation aspects.
Rocznik
Tom
Strony
825--830
Opis fizyczny
Bibliogr. 42 poz., rys., tab.
Twórcy
autor
- King Faisal University, Al-Ahsa, Saudi Arabia
Bibliografia
- [1] Aidi, S., & Mazouzi, M. (2023). Optimization Approach for Yard Crane Scheduling Problem using Genetic Algorithm in Container Terminals. ITM Web of Conferences, 52, 02002. https://doi.org/10.1051/itmconf/20235202002
- [2] Al Samrout, M., Sbihi, A., & Yassine, A. (2024). An improved genetic algorithm for the berth scheduling with ship-to-ship transshipment operations integrated model. Computers & Operations Research, 161, 106409. https://doi.org/10.1016/j.cor.2023.106409
- [3] Ambrosino, D., & Xie, H. (2022). Optimization approaches for defining storage strategies in maritime container terminals. Soft Computing, 27(7), 4125–4137. https://doi.org/10.1007/s00500-022-06769-7
- [4] Bai, X., Yang, D., Yuen, K. F., & Wu, J. (2022). A deep learning approach for port congestion estimation and prediction. Maritime Policy & Management, 50(7), 835–860. https://doi.org/10.1080/03088839.2022.2057608
- [5] Benkert, J., Maack, R., & Meisen, T. (2023). Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System. Journal of Marine Science and Engineering, 11(9), 1718. https://doi.org/10.3390/jmse11091718
- [6] Boschma, R., Mes, M. R. K., & de Vries, L. R. (2023). Approximate dynamic programming for container stacking. European Journal of Operational Research, 310(1), 328–342. https://doi.org/10.1016/j.ejor.2023.02.034
- [7] Cao, Y., Yang, A., Liu, Y., Zeng, Q., & Chen, Q. (2023). AGV dispatching and bidirectional conflict-free routing problem in automated container terminal. Computers & Industrial Engineering, 184, 109611. https://doi.org/10.1016/j.cie.2023.109611
- [8] Cartenì, A., & Luca, S. de. (2012). Tactical and strategic planning for a container terminal: Modelling issues within a discrete event simulation approach. Simulation Modelling Practice and Theory, 21(1), 123–145. https://doi.org/10.1016/j.simpat.2011.10.005
- [9] Chang, D., & Chen, C.-H. (2023). A digital twin-based approach for optimizing operation energy consumption at automated container terminals. Journal of Cleaner Production, 385, 135782. https://doi.org/10.1016/j.jclepro.2022.135782
- [10] Chen, S., Zeng, Q., & Li, Y. (2023). Integrated operations planning in highly electrified container terminals considering time-of-use tariffs. Transportation Research Part E: Logistics and Transportation Review, 171, 103034. https://doi.org/10.1016/j.tre.2023.103034
- [11] Drungilas, D., Kurmis, M., Senulis, A., Lukosius, Z., Andziulis, A., Januteniene, J., Bogdevicius, M., Jankunas, V., & Voznak, M. (2023). Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal. Alexandria Engineering Journal, 67, 397–407. https://doi.org/10.1016/j.aej.2022.12.057
- [12] Fazi, S., Choudhary, S. K., & Dong, J.-X. (2023). The multi-trip container drayage problem with synchronization for efficient empty containers re-usage. European Journal of Operational Research, 310(1), 343–359. https://doi.org/10.1016/j.ejor.2023.02.041
- [13] Gao, Y., & Ge, Y.-E. (2022). Integrated scheduling of yard cranes, external trucks, and internal trucks in maritime container terminal operation. Maritime Policy & Management, 50(5), 629–650. https://doi.org/10.1080/03088839.2022.2135177
- [14] Gao, Y., Chang, D., & Chen, C.-H. (2023). A digital twin-based approach for optimizing operation energy consumption at automated container terminals. Journal of Cleaner Production, 385, 135782. https://doi.org/10.1016/j.jclepro.2022.135782
- [15] Gumuskaya, V., van Jaarsveld, W., Dijkman, R., Grefen, P., & Veenstra, A. (2020). Dynamic barge planning with stochastic container arrivals. Transportation Research Part E: Logistics and Transportation Review, 144, 102161. https://doi.org/10.1016/j.tre.2020.102161
- [16] Giallombardo, G., Moccia, L., Salani, M., & Vacca, I. (2010). Modeling and solving the Tactical Berth Allocation Problem. Transportation Research Part B: Methodological, 44(2), 232–245. https://doi.org/10.1016/j.trb.2009.07.003
- [17] Hu, H., Yang, X., Xiao, S., & Wang, F. (2021). Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning. International Journal of Production Research, 61(1), 65–80. https://doi.org/10.1080/00207543.2021.1998695
- [18] Huang, C., & Zhang, R. (2023). Container Drayage Transportation Scheduling With Foldable and Standard Containers. IEEE Transactions on Engineering Management, 70(10), 3497–3511. https://doi.org/10.1109/tem.2021.3094994
- [19] Hsu, H.-P., Chou, C.-C., & Wang, C.-N. (2022). Heuristic/Metaheuristic-Based Simulation Optimization Approaches for Integrated Scheduling of Yard Crane, Yard Truck, and Quay Crane Considering Import and Export Containers. IEEE Access, 10, 64650–64670. https://doi.org/10.1109/access.2022.3180752
- [20] Li, X., Peng, Y., Guo, Y., Wang, W., & Song, X. (2023). An integrated simulation and AHP-entropy-based NR-TOPSIS method for automated container terminal layout planning. Expert Systems with Applications, 225, 120197. https://doi.org/10.1016/j.eswa.2023.120197
- [21] Liu, G., Chang, D., & Wen, F. (2022). Research on the Beibu Gulf Port Container Terminal Operation System Construction Performance Evaluation Based on the AISM-ANP. Journal of Marine Science and Engineering, 10(11), 1574. https://doi.org/10.3390/jmse10111574
- [22] Mili, K. (2024). Optimizing Supply Chain Network Design Under Uncertainty: A Practical Methodology for Sustainable Value Creation. Journal of Ecohumanism, 3(3), 1574–1586. https://doi.org/10.62754/joe.v3i3.3330
- [23] MILI, Khaled. (2023). Dynamic container relocation problem. Journal of Maritime Research, Vol. 21(No. 1), 23–29. https://www.jmr.unican.es/index.php/jmr/article/ view/754
- [24] Mili, Khaled. (2024). Container Classification: A Hybrid AHP-CNN Approach for Efficient Logistics Management. Journal of Maritime Research, Vol. 21(No. 2), 381–388. https://www.jmr.unican.es/index.php/jmr/ article/view/666
- [25] MILI, K. and GASSARA, M. Multiple Straddle Carrier Routing Problem. Journal of Maritime Research, [S.l.], v. 12, n. 2, p. 63-70, (2017). ISSN 1697-9133. https://www.jmr.unican.es/index.php/jmr/article/view/303.
- [26] Mili, K. (2014). Six Sigma Approach for the Straddle Carrier Routing Problem. Procedia - Social and Behavioral Sciences, 111, 1195–1205. https://doi.org/10.1016/j.sbspro.2014.01.154
- [27] Mili, K. (2017). Solving the straddle carrier routing problem using Six Sigma methodology. International Journal of Process Management and Benchmarking, 7(3), 371. https://doi.org/10.1504/ijpmb.2017.084909
- [28] Mili, K., & Mili, F. (2012). Genetic procedure for the Single Straddle Carrier Routing Problem. International Journal of Advanced Computer Science and Applications, 3(11). https://doi.org/10.14569/ijacsa.2012.031104
- [29] Nguyen, S., Chen, P. S.-L., & Du, Y. (2023). Blockchain adoption in container shipping: An empirical study on barriers, approaches, and recommendations. Marine Policy, 155, 105724. https://doi.org/10.1016/j.marpol.2023.105724
- [30] Peng, W., Bai, X., Yang, D., Yuen, K. F., & Wu, J. (2022). A deep learning approach for port congestion estimation and prediction. Maritime Policy & Management, 50(7), 835–860. https://doi.org/10.1080/03088839.2022.2057608
- [31] Raeesi, R., Sahebjamnia, N., & Mansouri, S. A. (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, 310(3), 943–973. https://doi.org/10.1016/j.ejor.2022.11.054
- [32] Steenken, D., Voß, S. & Stahlbock, R. Container terminal operation and operations research a classification and literature review. OR Spectrum 26, 3–49 (2004). https://doi.org/10.1007/s00291-003-0157-z
- [33] Tang, X., Liu, C., Li, X., & Ji, Y. (2023). Distributionally Robust Programming of Berth-Allocation-with-Crane-Allocation Problem with Uncertain Quay-Crane-Handling Efficiency. Sustainability, 15(18), 13448. https://doi.org/10.3390/su151813448
- [34] Tao, Y., Zhang, S., Lin, C., & Lai, X. (2023). A bi-objective optimization for integrated truck operation and storage allocation considering traffic congestion in container terminals. Ocean & Coastal Management, 232, 106417. https://doi.org/10.1016/j.ocecoaman.2022.106417
- [35] Vallada, E., Belenguer, J. M., Villa, F., & Alvarez-Valdes, R. (2023). Models and algorithms for a yard crane scheduling problem in container ports. European Journal of Operational Research, 309(2), 910–924. https://doi.org/10.1016/j.ejor.2023.01.047
- [36] Wang, Y.-Z., Hu, Z.-H., & Tian, X.-D. (2024). Scheduling ASC and AGV considering direct, buffer, and hybrid modes for transferring containers. Computers & Operations Research, 161, 106419. https://doi.org/10.1016/j.cor.2023.106419
- [37] Weerasinghe, B.A., Perera, H.N. & Bai, X. Optimizing container terminal operations: a systematic review of operations research applications. Marit Econ Logist (2023). https://doi.org/10.1057/s41278-023-00254-0
- [38] Weerasinghe, B. A., Perera, H. N., & Kießner, P. (2022). Planning decision alterations and container terminal efficiency. Maritime Business Review, 8(1), 65–79. https://doi.org/10.1108/mabr-04-2021-0035
- [39] Xiang, X., Lee, L. H., & Chew, E. P. (2023). An Adaptive Dynamic Scheduling Policy for the Integrated Optimization Problem in Automated Transshipment Hubs. IEEE Transactions on Automation Science and Engineering, 1–15. https://doi.org/10.1109/tase.2023.3267448
- [40] Yang, Z.-Z., Chen, G., & Song, D.-P. (2013). Integrating truck arrival management into tactical operation planning at container terminals. Polish Maritime Research, 20(Special-Issue), 32–46. https://doi.org/10.2478/pomr-2013-0025
- [41] Zhang, M., & Ji, C. (2023). Dynamic scheduling optimization of AGVs in automated container terminals under uncertainty. Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023). https://doi.org/10.1117/12.2689849
- [42] Zhong, J., & Jiang, H. (2023). Optimization of container space allocation in automated terminal yards. Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023). https://doi.org/10.1117/12.2690044
Uwagi
1. Pełne imiona podano na stronie internetowej czasopisma w "Authors in other databases."
2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7925b4fe-f96a-49eb-941f-3b53c88be483
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