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Title: | 哥倫比亞港口作業的人工智慧解決方案:促進經濟發展、環境永續與營運效率 Artificial Intelligence Solutions in Port Operations for Economic Development, Environmental Sustainability, and Operational Efficiency: A Qualitative Study on Colombian Ports |
Authors: | 羅俊杰 Haya, Mario |
Contributors: | 史蘭亭 Alicia Say 羅俊杰 Haya, Mario |
Keywords: | 人工智慧 港口物流 港口效率 永續發展 發展中國家 Artificial Intelligence port logistics port efficiency sustainable development developing economies |
Date: | 2025 |
Issue Date: | 2025-09-01 15:22:23 (UTC+8) |
Abstract: | 人工智慧技術(包括機器學習與人工神經網路)正日益被應用於全球海運物流與供應鏈管理,以提升營運效率。然而,對現有文獻中關於人工智慧在港口營運應用的回顧發現,研究大多集中於高度發展及技術先進的港口,例如鹿特丹港與釜山港,卻普遍忽略了全球絕大多數港口所面臨的實際與財務限制。這種現象導致一種假設,即任何港口皆可輕易採用先進技術,而不論其財務或基礎建設條件,進而造成了對發展中國家(如哥倫比亞)港口在實務運作上的現實困境的研究缺口。為填補此一缺口,本研究針對哥倫比亞港口的營運、基礎設施與財務挑戰進行探討,聚焦於發展中國家特有的問題,例如財務資源有限、基礎設施不足,以及環境議題(包括保護環繞布埃納文圖拉港的紅樹林)。本研究採用質性研究方法,特別是紮根理論,並於哥倫比亞多個港區訪談了港口員工、海運運輸主管與船運業者。研究結果指出,哥倫比亞具備運用人工智慧推動永續發展的獨特潛力,尤其是在港口「橫向作業」如貨櫃調度與運輸方面,而在「縱向作業」如吊車自動化的應用則較為有限。此研究有助於深化對財務受限及環境敏感情境下,人工智慧應用於港口營運的理解。 Artificial Intelligence technologies, including Machine Learning and Artificial Neural Networks, are increasingly being integrated into global maritime logistics and supply chain management to enhance operational efficiency. However, a review of existing literature on AI applications in port operations revealed a disproportionate focus on highly developed and technologically advanced ports, such as the Port of Rotterdam and the Port of Busan, while largely neglecting the practical and financial constraints faced by the vast majority of ports worldwide. This has fostered the assumption that any port can readily adopt advanced technologies, regardless of financial or infrastructural limitations, thereby creating a research gap on the practical realities faced by ports in developing economies, such as Colombia. To address this gap, this study examines the operational, infrastructural, and financial challenges of Colombian ports, focusing on issues specific to developing economies, such as limited financial resources, inadequate infrastructure, and environmental concerns, including the protection of mangroves surrounding the Port of Buenaventura. Through a qualitative research approach, specifically grounded theory, this study conducted a series of interviews with port staff, maritime transport supervisors, and shipping operators across various terminals in Colombia. The findings revealed Colombia's unique potential to harness AI for sustainable development, particularly in ports' `horizontal operations', such as container scheduling and transport, and, to a lesser extent in `vertical operations' like crane automation. This ultimately contributes to a better understanding of AI implementations in financially constrained setting, as well as environmentally sensitive contexts. |
Reference: | AbuAlhaol, I., Falcon, R., Abielmona, R., & Petriu, E. (2018). Mining port congestion indicators from big ais data. 2018 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN.2018.8489187
Acciaro, M., Ferrari, C., Lam, J. S., Macario, R., Roumboutsos, A., Sys, C., Tei, A., & and, T. V. (2018). Are the innovation processes in seaport terminal operations successful? Maritime Policy & Management, 45(6), 787–802. https://doi.org/10.1080/03088839.2018.1466062
Agussurja, L., Kumar, A., & Lau, H. C. (2018). Resource-constrained scheduling for maritime traffic management. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12086
Alzahrani, S. M. (2022). Implementing green port strategies in saudi ports to achieve environ-mental sustainability. https://commons.wmu.se/cgi/viewcontent.cgi?article=3129&context=all_dissertations
An, Y., & Park, N. (2021). Economic analysis for investment of public sector’s automated container terminal: Korean case study. Journal of Marine Science and Engineering, 9(5). https://doi.org/10.3390/jmse9050459
Attiany, M., Al-Kharabsheh, S., Abed-Qader, M., Al-Hawary, S., Mohammad, A., & Raham-neh, A. (2023). Barriers to adopt industry 4.0 in supply chains using interpretive struc-tural modeling. Uncertain Supply Chain Management, 11(1), 299–306.
Baquero Villamil, G. A. (2022). Efectos del uso de tecnología de inspección no intrusiva en lagestión logística portuaria de cartagena conllevan a una mayor competitividad del país.
Behdani, B. (2023). Port 4.0: A conceptual model for smart port digitalization. Transportation Research Procedia, 74, 346–353.
Brancaccio, G., Kalouptsidi, M., & Papageorgiou, T. (2024). Investment in infrastructure and trade: The case of ports (tech. rep.). National Bureau of Economic Research.
Buduma, N., & Locascio, N. (2022). Fundamentals of deep learning: Designing next-generation machine intelligence algorithms (2nd ed.). O’Reilly Media. https://www.amazon.com/Fundamentals-Deep-Learning-Next-Generation-Intelligence/dp/149208218X
Cerutti, E. M., Garcia-Pascual, A. I., Kido, Y., Li, L., Melina, G., Tavares, M. M., & Wingender, P. (2025). The global impact of ai: Mind the gap (tech. rep. No. 2025/076). International Monetary Fund. https://doi.org/10.5089/9798229008570.001
Charmaz, K. (2014). Constructing grounded theory (2nd). SAGE Publications.
Chen, Q., Wang, J., & Lin, J. (2025). Generative ai exacerbates the climate crisis. Science,387(6734), 587–587. https://doi.org/10.1126/science.adt5536
Chen, Q. (2023). The impact of agv application on port operating efficiency. Theoretical and Natural Science, 18, 19–29. https://www.ewadirect.com/proceedings/tns/article/view/8378
Chu, Z., Yan, R., & Wang, S. (2024). Vessel turnaround time prediction: A machine learning approach. Ocean & Coastal Management, 249, 107021. https://doi.org/https://doi.org/10.1016/j.ocecoaman.2024.107021
Cruz, T. M., Park, J., Moore, E., Chen, A., & Gordillo, A. (2023). Algorithms in the margins: Organized community resistance to port automation in the los angeles harbor area. En-gaging Science, Technology, and Society, 9(3), 32–52.
D’Amico, G., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Smart and sus-tainable logistics of port cities: A framework for comprehending enabling factors, do-mains and goals. Sustainable Cities and Society, 69, 102801. https://doi.org/https: //doi.org/10.1016/j.scs.2021.102801
De Alwis, N., & Nam, H.-S. (2024). A way towards port automation: Challenges and implica-tions. WMU Journal of Maritime Affairs, 1–23.
Dhingra, V., Roy, D., & de Koster, R. B. (2017). A cooperative quay crane-based stochastic model to estimate vessel handling time. Flexible services and manufacturing journal, 29, 97–124.
Dinh, G. H., Pham, H. T., Nguyen, L. C., Dang, H. Q., & Pham, N. D. K. (2024). Leveraging artificial intelligence to enhance port operation efficiency. Polish Maritime Research.
Dkhil, H., Diarrassouba, I., Benmansour, S., & Yassine, A. (2021). Modelling and solving a berth allocation problem in an automotive transshipment terminal. Journal of the Oper-ational Research Society, 72(3), 580–593.
Donázar-Aramendía, I., Megina, C., Miró, J., Florido, M., Reyes-Martínez, M., Olaya-Ponzone, L., & García-Gómez, J. (2024). Environmental effects of maintenance dredging works in a highly modified estuary: A short-term approach. Ocean & Coastal Management, 258, 107394. https://doi.org/https://doi.org/10.1016/j.ocecoaman.2024.107394
Drungilas, D., Kurmis, M., Senulis, A., Lukosius, Z., Andziulis, A., Januteniene, J., Bogdevi-cius, M., Jankunas, V., & Voznak, M. (2023). Deep reinforcement learning based op-timization of automated guided vehicle time and energy consumption in a container terminal. Alexandria Engineering Journal, 67, 397–407. https://doi.org/https://doi.org/10.1016/j.aej.2022.12.057
Ducruet, C., Polo Martin, B., Sene, M. A., Lo Prete, M., Sun, L., Itoh, H., & Pigné, Y. (2024). Ports and their influence on local air pollution and public health: A global analysis [Epub 2024 Jan 13]. Science of The Total Environment, 915, 170099. https://doi.org/10.1016/j.scitotenv.2024.170099
El Colombiano. (2024, October). El tesoro que ha salvado a colombia del apagón [Accessed:2025-05-31]. https://www.elcolombiano.com/negocios/por- que-colombia- se-ha-salvado-del-apagon-energetico-OE25741087
Fadnes, E., & Harviken, E. Å. (2023). Using machine learning to predict port congestion: A study of the port of paranaguá [Master’s thesis].
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for quali-tative research. Aldine Publishing Company.
Gupta, S., Modgil, S., Choi, T.-M., Kumar, A., & Antony, J. (2023). Influences of artificial intelligence and blockchain technology on financial resilience of supply chains. Inter-national Journal of Production Economics, 261, 108868. https://doi.org/https://doi.org/10.1016/j.ijpe.2023.108868
Heikkilä, M., Saarni, J., & Saurama, A. (2022). Innovation in smart ports: Future directions of digitalization in container ports. Journal of Marine Science and Engineering, 10(12), 1925.
Homayouni, S. M., & Tang, S. H. (2016). Optimization of integrated scheduling of handling and storage operations at automated container terminals. WMU Journal of Maritime Affairs, 15, 17–39.
Hurtado, J. L. M., & Gómez, L. E. N. (2018). Factores internos que afectan la competitividad internacional del puerto de buenaventura, colombia. Libre empresa, 15(1), 103–118.
Inkinen, T., Helminen, R., & Saarikoski, J. (2021). Technological trajectories and scenarios in seaport digitalization. Research in Transportation Business and Management, 41,100633. https://doi.org/https://doi.org/10.1016/j.rtbm.2021.100633
International Energy Agency. (2023). Colombia - country profile [Accessed: 2025-05-31]. https://www.iea.org/countries/colombia
Iris, Ç., & Lam, J. S. L. (2019). A review of energy efficiency in ports: Operational strategies, technologies and energy management systems. Renewable and Sustainable Energy Re-views, 112, 170–182. https://doi.org/https://doi.org/10.1016/j.rser.2019.04.069
Jeon, S. M., Kim, K. H., & Kopfer, H. (2011). Routing automated guided vehicles in container terminals through the q-learning technique. Logistics Research, 3(1), 19–27. https://doi.org/10.1007/s12159-010-0042-5
Jin, X., Mi, N., Song, W., & Li, Q. (2024). Scheduling of twin automated stacking cranes based on deep reinforcement learning. Computers & Industrial Engineering, 191, 110104.
Karsavran, Y., & Erdik, T. (2021). Artificial intelligence based prediction of seawater level: A case study for bosphorus strait. International Journal of Mathematical, Engineering and Management Sciences, 6(5), 1242.
Kim, B., Kim, G., & Kang, M. (2022). Study on comparing the performance of fully automated container terminals during the covid-19 pandemic. Sustainability, 14(15). https://doi.org/10.3390/su14159415
Le, S.-T., & Nguyen, T.-H. (2023). The development of green ports in emerging nations: A case study of vietnam. Sustainability, 15(18). https://doi.org/10.3390/su151813502
Lee, H., Chatterjee, I., & Cho, G. (2023). Ai-powered intelligent seaport mobility: Enhancing container drayage efficiency through computer vision and deep learning. Applied Sciences, 13(22). https://doi.org/10.3390/app132212214
Lehmacher, W., Lind, M., Poikonen, J., Meseguer, J., & and, J. L. C. C. (2022). Reducing port city congestion through data analysis, simulation, and artificial intelligence to improve the well-being of citizens. Journal of Mega Infrastructure & Sustainable Development, 2(sup1), 65–82. https://doi.org/10.1080/24724718.2022.2133524
Lombard, M., Hernández-García, J., & Salgado-Ramírez, I. (2023). Beyond displacement: Territorialization in the port city of buenaventura, colombia. Territory, Politics, Governance, 11(7), 1324–1343.
Lu, H., & Wang, S. (2019). A study on multi-asc scheduling method of automated container terminals based on graph theory. Computers & Industrial Engineering, 129, 404–416. https://doi.org/https://doi.org/10.1016/j.cie.2019.01.050
Machucho, R., & Ortiz, D. (2025). The impacts of artificial intelligence on business innovation: A comprehensive review of applications, organizational challenges, and ethical considerations. Systems, 13(4), 264.
Mongelluzzo, B. (2024). More north american port automation expected. Journal of Commerce. https://www.joc.com/article/more-north-american-port-automation-expected-5208911
Munim, Z. H., & Schramm, H.-J. (2018). The impacts of port infrastructure and logistics performance on economic growth: The mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1. https://doi.org/10.1186/s41072-018-0027-0
Nacht, M., & Henry, L. (2022). Terminal automation in southern california: Implications for growth, jobs, and the future competitiveness of west coast ports. Pacific Maritime Association. https://gspp.berkeley.edu/assets/uploads/research/pdf/Nacht_and_Henry_Automation_Report_FINAL_w_page_numbers.pdf#:~:text=move%20containers%20around%20the%20terminal,higher
Nadi, A., Sharma, S., Snelder, M., Bakri, T., van Lint, H., & Tavasszy, L. (2021). Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of rotterdam. Transportation Research Part C: Emerging Technologies, 127, 103111. https://doi.org/https://doi.org/10.1016/j.trc.2021.103111
Notteboom, T., Pallis, A., & Rodrigue, J.-P. (2022). Port economics, management and policy. Routledge.
OECD. (2024). Oecd economic surveys: Colombia 2024 (Accessed: 2025-05-31). Organisation for Economic Co-operation and Development. https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/09/oecd- economic- surveys- colombia- 2024_7b382d76/a1a22cd6-en.pdf
Oliveira, H., & Varela, R. (2017). Automation in ports and labour relations in xxi century.
Oluwaferanmi, A. (2025). The role of port automation and smart technologies in reducing cargo dwell times and enhancing supply chain efficiency: A case study of tema port in ghana.
Peng, W., Bai, X., Yang, D., Yuen, K. F., & Wu, J. (2023). A deep learning approach for portcongestion estimation and prediction. Maritime Policy & Management, 50(7), 835–860. https://doi.org/10.1080/03088839.2022.2057608
Safuan, S., & Syafira, A. (2024). Artificial intelligence in indonesian ports: Opportunities and challenges. Transactions on Maritime Science, 13(2). https://doi.org/10.7225/toms.v13. n02.w07
Saravia de los Reyes, R., Fernández-Sánchez, G., Esteban, M. D., & Rodríguez, R. R. (2020). Carbon footprint of a port infrastructure from a life cycle approach. International Journal of Environmental Research and Public Health, 17(20), 7414.
Sim, S., Kim, D., Park, K., & Bae, H. (2024). Artificial intelligence-based smart port logistics metaverse for enhancing productivity, environment, and safety in port logistics: A case study of busan port. https://arxiv.org/abs/2409.10519
Široka, M., Piličić, S., Milošević, T., Lacalle, I., & Traven, L. (2021). A novel approach for assessing the ports’environmental impacts in real time –the iot based port environmental index. Ecological Indicators, 120, 106949. https://doi.org/https://doi.org/10.1016/j.ecolind.2020.106949
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in nlp. https://arxiv.org/abs/1906.02243
Superintendency of Transport. (2025, February). Statistical bulletin: Port traffic in colombia [Data from January to December 2024]. https://www.supertransporte.gov.co/documentos/2025/marzo/puertos_06/BOLETIN-ESTADISTICO-TRAFICO-PORTUARIO-EN-COLOMBIA-ENERO-A-DICIEMBRE-2024.pdf
Talley, W. K. (2006). Port performance: An economics perspective. Research in Transportation Economics, 17, 499–516.
Tang, Q., & Wang, H. (2025). Data-driven automated job shop scheduling optimization considering agv obstacle avoidance. Scientific Reports, 15(1), 5. https://doi.org/10.1038/s41598-024-82870-1
Tiwari, S., Sharma, P., Choi, T.-M., & Lim, A. (2023). Blockchain and third-party logistics for global supply chain operations: Stakeholders’perspectives and decision roadmap.
Transportation Research Part E: Logistics and Transportation Review, 170, 103012. https://doi.org/https://doi.org/10.1016/j.tre.2022.103012
Tsolakis, N., Zissis, D., Papaefthimiou, S., & Korfiatis, N. (2022). Towards ai driven environmental sustainability: An application of automated logistics in container port terminals. International Journal of Production Research, 60(14), 4508–4528.
U.S. International Trade Administration. (2023). Colombia - infrastructure [Accessed: 2025-06-04]. https://www.trade.gov/country-commercial-guides/colombia-infrastructure
Vega, L., Cantillo, V., & Arellana, J. (2019). Assessing the impact of major infrastructure projects on port choice decision: The colombian case. Transportation Research Part A: Policy and Practice, 120, 132–148. https://doi.org/https://doi.org/10.1016/j.tra.2018.12.021
Virginia Port Authority. (2023, September). Port of virginia begins $650 million north berth modernization [FY 2023 Annual Comprehensive Financial Report]. Virginia Port Authority.
Witte, P., Slack, B., Keesman, M., Jugie, J.-H., & Wiegmans, B. (2018). Facilitating start-ups in port-city innovation ecosystems: A case study of montreal and rotterdam. Journal of Transport Geography, 71, 224–234. https://doi.org/https://doi.org/10.1016/j.jtrangeo.2017.03.006
World Bank. (2024). Regional disruptions drive changes in global container port performance ranking [Accessed: 2025-06-08]. https://www.worldbank.org/en/news/press-release/2024/06/01/regional-disruptions-drive-changes-in-global-container-port-performance-ranking
World Bank. (2025). Gini index [https://data.worldbank.org/indicator/SI.POV.GINI?most_recent_year_desc=true&year=2023].
Xu, H., Liu, J., Xu, X., Chen, J., & Yue, X. (2024). The impact of ai technology adoption on operational decision-making in competitive heterogeneous ports. Transportation Research Part E: Logistics and Transportation Review, 183, 103428. https://doi.org/https://doi.org/10.1016/j.tre.2024.103428
Xu, Y., Qi, L., Luan, W., Guo, X., & Ma, H. (2020). Load-in-load-out agv route planning in automatic container terminal. Ieee Access, 8, 157081–157088.
Yang, T.-J., Chen, Y.-H., & Sze, V. (2017). Designing energy-efficient convolutional neural networks using energy-aware pruning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://openaccess.thecvf.com/content_cvpr_2017/html/Yang_Designing_Energy-Efficient_Convolutional_CVPR_2017_paper.html
Zheng, X., Liang, C., Wang, Y., Shi, J., & Lim, G. (2022). Multi-agv dynamic scheduling in an automated container terminal: A deep reinforcement learning approach. Mathematics, 10, 4575. https://doi.org/10.3390/math10234575
Zhou, S., Yu, Y., Zhao, M., Zhuo, X., Lian, Z., & Zhou, X. (2025). A reinforcement learning-based agv scheduling for automated container terminals with resilient charging strate-gies. IET Intelligent Transport Systems, 19(1), e70027. https://doi.org/https://doi.org/10.1049/itr2.70027 |
Description: | 碩士 國立政治大學 應用經濟與社會發展英語碩士學位學程(IMES) 112266017 |
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