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Applications of machine learning methods in port operations – A systematic literature review

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  • Filom, Siyavash
  • Amiri, Amir M.
  • Razavi, Saiedeh

Abstract

Ports are pivotal nodes in supply chain and transportation networks, in which most of the existing data remain underutilized. Machine learning methods are versatile tools to utilize and harness the hidden power of the data. Considering ever-growing adoption of machine learning as a data-driven decision-making tool, the port industry is far behind other modes of transportation in this transition. To fill the gap, we aimed to provide a comprehensive systematic literature review on this topic to analyze the previous research from different perspectives such as area of the application, type of application, machine learning method, data, and location of the study. Results showed that the number of articles in the field has been increasing annually, and the most prevalent use case of machine learning methods is to predict different port characteristics. However, there are emerging prescriptive and autonomous use cases of machine learning methods in the literature. Furthermore, research gaps and challenges are identified, and future research directions have been discussed from method-centric and application-centric points of view.

Suggested Citation

  • Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:transe:v:161:y:2022:i:c:s1366554522001132
    DOI: 10.1016/j.tre.2022.102722
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    1. Gianfranco Fancello & Claudia Pani & Marco Pisano & Patrizia Serra & Paola Zuddas & Paolo Fadda, 2011. "Prediction of arrival times and human resources allocation for container terminal," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 13(2), pages 142-173, June.
    2. Ku, Dusan & Arthanari, Tiru S., 2016. "Container relocation problem with time windows for container departure," European Journal of Operational Research, Elsevier, vol. 252(3), pages 1031-1039.
    3. Cheng-Hong Yang & Po-Yin Chang, 2020. "Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    4. Leonard Heilig & Stefan Voß, 2017. "Information systems in seaports: a categorization and overview," Information Technology and Management, Springer, vol. 18(3), pages 179-201, September.
    5. Dong Yang & Lingxiao Wu & Shuaian Wang & Haiying Jia & Kevin X. Li, 2019. "How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 755-773, November.
    6. Zhen, Lu & Lee, Loo Hay & Chew, Ek Peng, 2011. "A decision model for berth allocation under uncertainty," European Journal of Operational Research, Elsevier, vol. 212(1), pages 54-68, July.
    7. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    8. Caballini, Claudia & Gracia, Maria D. & Mar-Ortiz, Julio & Sacone, Simona, 2020. "A combined data mining – optimization approach to manage trucks operations in container terminals with the use of a TAS: Application to an Italian and a Mexican port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    9. Kevin Tierney & Dario Pacino & Stefan Voß, 2017. "Solving the pre-marshalling problem to optimality with A* and IDA," Flexible Services and Manufacturing Journal, Springer, vol. 29(2), pages 223-259, June.
    10. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    11. Ha, Min-Ho & Yang, Zaili & Notteboom, Theo & Ng, Adolf K.Y. & Heo, Man-Wook, 2017. "Revisiting port performance measurement: A hybrid multi-stakeholder framework for the modelling of port performance indicators," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 1-16.
    12. Moros-Daza, Adriana & Amaya-Mier, René & Paternina-Arboleda, Carlos, 2020. "Port Community Systems: A structured literature review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 27-46.
    13. Rashed, Yasmine & Meersman, Hilde & Sys, Christa & Van de Voorde, Eddy & Vanelslander, Thierry, 2018. "A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 127-141.
    14. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    15. Lokuge, Prasanna & Alahakoon, Damminda, 2007. "Improving the adaptability in automated vessel scheduling in container ports using intelligent software agents," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1985-2015, March.
    16. Ümit Gökkuş & Mehmet Sinan Yıldırım & Metin Mutlu Aydin, 2017. "Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-15, March.
    17. Jaecheul Lee, 2019. "Deep learning–assisted real-time container corner casting recognition," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    18. Ding, Ding & Chou, Mabel C., 2015. "Stowage planning for container ships: A heuristic algorithm to reduce the number of shifts," European Journal of Operational Research, Elsevier, vol. 246(1), pages 242-249.
    19. Christiaan Heij & Sabine Knapp, 2019. "Shipping inspections, detentions, and incidents: an empirical analysis of risk dimensions," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(7), pages 866-883, October.
    20. Fotuhi, Fateme & Huynh, Nathan & Vidal, Jose M. & Xie, Yuanchang, 2013. "Modeling yard crane operators as reinforcement learning agents," Research in Transportation Economics, Elsevier, vol. 42(1), pages 3-12.
    21. Heilig, Leonard & Lalla-Ruiz, Eduardo & Voß, Stefan, 2017. "Multi-objective inter-terminal truck routing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 178-202.
    22. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    23. Nadereh Moini & Maria Boile & Sotiris Theofanis & William Laventhal, 2012. "Estimating the determinant factors of container dwell times at seaports," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(2), pages 162-177, June.
    24. Ziaul Haque Munim & Mariia Dushenko & Veronica Jaramillo Jimenez & Mohammad Hassan Shakil & Marius Imset, 2020. "Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 577-597, July.
    25. Önsel Ekici, Şule & Kabak, Özgür & Ülengin, Füsun, 2016. "Linking to compete: Logistics and global competitiveness interaction," Transport Policy, Elsevier, vol. 48(C), pages 117-128.
    26. Hwarng, H. Brian & Ang, H. T., 2001. "A simple neural network for ARMA(p,q) time series," Omega, Elsevier, vol. 29(4), pages 319-333, August.
    27. Xie, Yuanchang & Huynh, Nathan, 2010. "Kernel-Based Machine Learning Methods for Modeling Daily Truck Volume at Seaport Terminals," 51st Annual Transportation Research Forum, Arlington, Virginia, March 11-13, 2010 207231, Transportation Research Forum.
    28. Wu, Yen-Chun Jim & Goh, Mark, 2010. "Container port efficiency in emerging and more advanced markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 1030-1042, November.
    29. Manav Kaushik & A K Giri, 2020. "Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques," Papers 2002.10247, arXiv.org.
    30. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    31. Christiansen, Marielle & Fagerholt, Kjetil & Nygreen, Bjørn & Ronen, David, 2013. "Ship routing and scheduling in the new millennium," European Journal of Operational Research, Elsevier, vol. 228(3), pages 467-483.
    32. Lepenioti, Katerina & Bousdekis, Alexandros & Apostolou, Dimitris & Mentzas, Gregoris, 2020. "Prescriptive analytics: Literature review and research challenges," International Journal of Information Management, Elsevier, vol. 50(C), pages 57-70.
    33. Chen, Rui & Meng, Qiang & Jia, Peng, 2022. "Container port drayage operations and management: Past and future," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    34. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    35. Zhang, Yuchang & Bai, Ruibin & Qu, Rong & Tu, Chaofan & Jin, Jiahuan, 2022. "A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties," European Journal of Operational Research, Elsevier, vol. 300(2), pages 418-427.
    36. Martijn R Van Der Horst & Peter W De Langen, 2008. "Coordination in Hinterland Transport Chains: A Major Challenge for the Seaport Community," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 10(1-2), pages 108-129, March.
    37. Umang, Nitish & Bierlaire, Michel & Vacca, Ilaria, 2013. "Exact and heuristic methods to solve the berth allocation problem in bulk ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 14-31.
    38. M. Milenković & N. Milosavljevic & N. Bojović & S. Val, 2021. "Container flow forecasting through neural networks based on metaheuristics," Operational Research, Springer, vol. 21(2), pages 965-997, June.
    39. Zhang, Canrong & Guan, Hao & Yuan, Yifei & Chen, Weiwei & Wu, Tao, 2020. "Machine learning-driven algorithms for the container relocation problem," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 102-131.
    40. Jae Hun Kim & Juyeon Kim & Gunwoo Lee & Juneyoung Park, 2021. "Machine Learning-Based Models for Accident Prediction at a Korean Container Port," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    41. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    42. Francesco Parola & Marcello Risitano & Marco Ferretti & Eva Panetti, 2017. "The drivers of port competitiveness: a critical review," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 116-138, January.
    43. Giusti, Riccardo & Manerba, Daniele & Bruno, Giorgio & Tadei, Roberto, 2019. "Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 92-110.
    44. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    45. Dirk Steenken & Stefan Voß & Robert Stahlbock, 2005. "Container terminal operation and operations research — a classification and literature review," Springer Books, in: Hans-Otto Günther & Kap Hwan Kim (ed.), Container Terminals and Automated Transport Systems, pages 3-49, Springer.
    46. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    47. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    48. Li Da Xu & Eric L. Xu & Ling Li, 2018. "Industry 4.0: state of the art and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2941-2962, April.
    49. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
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