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2023 Journal Impact Factor - 0.7
2023 CiteScore - 1.4
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Optimizing Container Terminal Operations: A Comparative Analysis of Hierarchical and Integrated Solution Approaches
K. Mili 1
1 King Faisal University, Al-Ahsa, Saudi Arabia
ABSTRACT: 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.
KEYWORDS: Container Terminal, Traffic Congestion, Port Operations, Energy Efficiency, Berth Allocation Problem (BAP), Quay Crane Assignment Problem (QCAP), Integrated Optimization, Operational Research
REFERENCES
Aidi, S., & Mazouzi, M. (2023). Optimization Approach for Yard Crane Scheduling Problem using Genetic Algorithm in Container Terminals. ITM Web of Conferences, 52, 02002. - doi:10.1051/itmconf/20235202002
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. - doi:10.1016/j.cor.2023.106409
Ambrosino, D., & Xie, H. (2022). Optimization approaches for defining storage strategies in maritime container terminals. Soft Computing, 27(7), 4125–4137. - doi:10.1007/s00500-022-06769-7
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. - doi:10.1080/03088839.2022.2057608
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. - doi:10.3390/jmse11091718
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. - doi:10.1016/j.ejor.2023.02.034
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. - doi:10.1016/j.cie.2023.109611
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. - doi:10.1016/j.simpat.2011.10.005
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. - doi:10.1016/j.jclepro.2022.135782
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. - doi:10.1016/j.tre.2023.103034
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. - doi:10.1016/j.aej.2022.12.057
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. - doi:10.1016/j.ejor.2023.02.041
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. - doi:10.1080/03088839.2022.2135177
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. - doi:10.1016/j.jclepro.2022.135782
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. - doi:10.1016/j.tre.2020.102161
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. - doi:10.1016/j.trb.2009.07.003
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. - doi:10.1080/00207543.2021.1998695
Huang, C., & Zhang, R. (2023). Container Drayage Transportation Scheduling With Foldable and Standard Containers. IEEE Transactions on Engineering Management, 70(10), 3497–3511. - doi:10.1109/TEM.2021.3094994
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. - doi:10.1109/ACCESS.2022.3180752
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. - doi:10.1016/j.eswa.2023.120197
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. - doi:10.3390/jmse10111574
Mili, K. (2024). Optimizing Supply Chain Network Design Under Uncertainty: A Practical Methodology for Sustainable Value Creation. Journal of Ecohumanism, 3(3), 1574–1586. - doi:10.62754/joe.v3i3.3330
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
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
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.
Mili, K. (2014). Six Sigma Approach for the Straddle Carrier Routing Problem. Procedia - Social and Behavioral Sciences, 111, 1195–1205. - doi:10.1016/j.sbspro.2014.01.154
Mili, K. (2017). Solving the straddle carrier routing problem using Six Sigma methodology. International Journal of Process Management and Benchmarking, 7(3), 371. - doi:10.1504/IJPMB.2017.084909
Mili, K., & Mili, F. (2012). Genetic procedure for the Single Straddle Carrier Routing Problem. International Journal of Advanced Computer Science and Applications, 3(11). - doi:10.14569/IJACSA.2012.031104
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. - doi:10.1016/j.marpol.2023.105724
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. - doi:10.1080/03088839.2022.2057608
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. - doi:10.1016/j.ejor.2022.11.054
Steenken, D., Voß, S. & Stahlbock, R. Container terminal operation and operations research a classification and literature review. OR Spectrum 26, 3–49 (2004). - doi:10.1007/s00291-003-0157-z
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. - doi:10.3390/su151813448
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. - doi:10.1016/j.ocecoaman.2022.106417
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. - doi:10.1016/j.ejor.2023.01.047
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. - doi:10.1016/j.cor.2023.106419
Weerasinghe, B.A., Perera, H.N. & Bai, X. Optimizing container terminal operations: a systematic review of operations research applications. Marit Econ Logist (2023). - doi:10.1057/s41278-023-00254-0
Weerasinghe, B. A., Perera, H. N., & Kießner, P. (2022). Planning decision alterations and container terminal efficiency. Maritime Business Review, 8(1), 65–79. - doi:10.1108/MABR-04-2021-0035
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. - doi:10.1109/TASE.2023.3267448
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. - doi:10.2478/pomr-2013-0025
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). - doi:10.1117/12.2689849
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). - doi:10.1117/12.2690044
Citation note:
Mili K.: Optimizing Container Terminal Operations: A Comparative Analysis of Hierarchical and Integrated Solution Approaches. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 18, No. 4, doi:10.12716/1001.18.04.09, pp. 825-830, 2024
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