Deniz Lojistiğinde Yapay Zeka Uygulamalarının Etkileri

Abstract

Bu çalışma, deniz taşımacılığı problemlerini çözmek için Yapay Zeka yöntemlerinin kullanımındaki güncel yaklaşımları belirlemeyi amaçlamaktadır. Yapay zekadaki son gelişmeler incelenerek deniz lojistiğine uyarlanma şekli gözden geçirilmektedir. Bu çalışmada denizcilik endüstrisinde yapay zeka ile ilgili 66 makale bibliyometrik olarak incelenmiştir. Araştırma verileri öncelikle IEEE Xplore, Web of Science, ScienceDirect (Elsevier), Sciences Citation Index, Google Scholar, Springer ve ilgili dergilerin veritabanlarından elde edilmiştir. Seçilen makaleler kategorize edilerek tasnif edilmiş ve bazı önemli yayınların sonuçları ayrıntılı olarak tartışılmıştır. Araştırma boşluklarını vurgulayan ve gelecekteki araştırma yönelimlerini tahmin eden kapsamlı bir değerlendirme de sunulmaktadır. Yapay zeka kullanan daha fazla araştırma için denizcilik endüstrisinde iki olası alan önerilmiştir. Tahmine dayalı analiz ilk alandır ve bunu enerji verimliliği optimizasyonu takip etmektedir. Buna ek olarak, Makine Öğrenmesi ve Yöneylem Araştırması yüksek düzeyde uzmanlaşmış buluşsal yöntemler oluşturmak için pahalı ve verimsiz insan emeğine duyulan ihtiyacı önlemek için optimizasyon sorunlarını çözmek için buluşsal yöntemlerin öğrenilmesini otomatikleştirmeye yönelik artan bir ilgiyi teşvik etmiştir. Gelecekteki araştırmalar, sürekli artan miktarda mevcut veriyi kullanarak Denizcilik Lojistiği sorunlarını ele almak için bu yeni makine öğrenmesi yaklaşımlarından yararlanabilir. Deniz lojistiği ile ilgili gelecekteki araştırmalar, belirlenen boşluklara dayalı öğrenme modelleri de geliştirebilir.

Keywords

• Yapay Zeka
• Otomatik Bilgi Sistemi
• Literatür Araştırması
• Makine Öğrenmesi
• Deniz Lojistiği

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Abstract

This study aims to identify current approaches in the usage of Artificial Intelligence (AI) methods for solving shipping problems. Recent advances in AI are being examined, and the way it is adapted to maritime logistics is reviewed. In this study, 66 papers dealing with AI in the maritime industry are reviewed bibliometrically. Research data were primarily sourced from databases of IEEE Xplore, Web of Science, ScienceDirect (Elsevier), Sciences Citation Index, Google Scholar, Springer, and journals. Selected papers are categorized and classified, and the outcomes of some noteworthy publications are discussed in detail. A comprehensive assessment is also presented, which highlights research gaps and forecasts future research orientations. Two possible areas in the maritime industry are proposed for further research using AI capabilities. Predictive analysis is the first domain, followed by energy efficiency optimization. In addition, Machine Learning (ML) and Operations Research (OR) have fostered a growing interest in automating the learning of heuristics to solve optimization problems to avoid the need for expensive and inefficient human labour to create highly specialized heuristics. Future research can take advantage of these new ML approaches to address Maritime Logistics problems utilizing the ever-increasing amount of data available. Future research on maritime logistics can also develop learning models based on the identified gaps.

Keywords

• Artificial Intelligence
• Automated Information System
• Literature Review
• Machine Learning
• Maritime Logistics

References

1. Abebe, M., Shin, Y., Noh, Y., Lee, S., & Lee, I. (2020). Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping. Applied Sciences, 10(7). doi:10.3390/app10072325
2. Adi, T. N., Iskandar, Y. A., & Bae, H. (2020). Interterminal Truck Routing Optimization Using Deep Reinforcement Learning. Sensors, 20(20). doi:10.3390/s20205794
3. Al Hajj Hassan, L., Mahmassani, H. S., & Chen, Y. (2020). Reinforcement learning framework for freight demand forecasting to support operational planning decisions. Transportation Research Part E: Logistics and Transportation Review, 137, 101926. doi:https://doi.org/10.1016/j.tre.2020.101926
4. Anwar, M., Henesey, L., & Casalicchio, E. (2019). Digitalization in Container Terminal Logistics : A Literature Review. Paper presented at the 27th Annual Conference of International Association of Maritime Economists, Athens. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18482
5. Brouer, B. D., Karsten, C. V., & Pisinger, D. (2017). Optimization in liner shipping. 4OR, 15(1), 1-35. doi:10.1007/s10288-017-0342-6
6. Ceyhun, G. Ç. (2020). Recent developments of artificial intelligence in business logistics: A maritime industry case. In Digital Business Strategies in Blockchain Ecosystems (pp. 343-353): Springer.
7. Chen, N., Ding, X., & Zhang, H. (2020). Improved Faster R-CNN identification method for containers. International Journal of Embedded Systems, 13(3), 308-317. doi:10.1504/IJES.2020.109968
8. Chen, X., Liu, Y., Achuthan, K., & Zhang, X. (2020). A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network. Ocean Engineering, 218, 108182 . doi:https://doi.org/10.1016/j.oceaneng.2020.108182

9. Cheng, C., Fallahi, K., Leung, H., & Tse, C. K. (2012). A Genetic Algorithm-Inspired UUV Path Planner Based on Dynamic Programming. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1128-1134. doi:10.1109/TSMCC.2011.2180526
10. de la Peña Zarzuelo, I., Freire Soeane, M. J., & López Bermúdez, B. (2020). Industry 4.0 in the port and maritime industry: A literature review. Journal of Industrial Information Integration, 20, 100173. doi:https://doi.org/10.1016/j.jii.2020.100173
11. Dornemann, J., Rückert, N., Fischer, K., & Taraz, A. (2020). Artificial intelligence and operations research in maritime logistics.
12. Du, P., Wang, J., Yang, W., & Niu, T. (2019). Container throughput forecasting using a novel hybrid learning method with error correction strategy. Knowledge-Based Systems, 182, 104853. doi:https://doi.org/10.1016/j.knosys.2019.07.024
13. Fikioris, G., Patroumpas, K., & Artikis, A. (2020, 30 June-3 July 2020). Optimizing Vessel Trajectory Compression. Paper presented at the 2020 21st IEEE International Conference on Mobile Data Management (MDM).
14. Filipiak, D., Węcel, K., Stróżyna, M., Michalak, M., & Abramowicz, W. (2020). Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm. Business & Information Systems Engineering, 62(5), 435-450. doi:10.1007/s12599-020-00661-0
15. Fruth, M., & Teuteberg, F. (2017). Digitization in maritime logistics—What is there and what is missing? Cogent Business & Management, 4(1), 1411066. doi:10.1080/23311975.2017.1411066
16. Gao, Y., Chang, D., Fang, T., & Fan, Y. (2019). The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network. Journal of Advanced Transportation, 2019, 5764602. doi:10.1155/2019/5764602
17. Han, P., & Yang, X. (2020). Big data-driven automatic generation of ship route planning in complex maritime environments. Acta Oceanologica Sinica, 39(8), 113-120. doi:10.1007/s13131-020-1638-5
18. Heilig, L., Lalla-Ruiz, E., & Voß, S. (2017). Digital transformation in maritime ports: analysis and a game theoretic framework. NETNOMICS: Economic Research and Electronic Networking, 18(2), 227-254. doi:10.1007/s11066-017-9122-x
19. Hoque, X., & Sharma, S. K. (2020). Ensembled deep learning approach for maritime anomaly detection system. In Proceedings of ICETIT 2019 (pp. 862-869): Springer.
20. Hu, Z.-H., Liu, C.-J., Chen, W., Wang, Y.-G., & Wei, C. (2020). Maritime convection and fluctuation between Vietnam and China: A data-driven study. Research in Transportation Business & Management, 34, 100414. doi:https://doi.org/10.1016/j.rtbm.2019.100414
21. Ji, C., & Lu, S. (2020). Exploration of marine ship anomaly real-time monitoring system based on deep learning. Journal of Intelligent & Fuzzy Systems, 38, 1235-1240. doi:10.3233/JIFS-179485
22. Jimenez, V. J., Bouhmala, N., & Gausdal, A. H. (2020). Developing a predictive maintenance model for vessel machinery. Journal of Ocean Engineering and Science, 5(4), 358-386. doi:https://doi.org/10.1016/j.joes.2020.03.003
23. Kamal, I. M., Bae, H., Sunghyun, S., & Yun, H. (2020). DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index. Applied Sciences, 10(4). doi:10.3390/app10041504
24. Kanamoto, K., Murong, L., Nakashima, M., & Shibasaki, R. (2021). Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers. Maritime Economics & Logistics, 23(2), 211-236. doi:10.1057/s41278-020-00171-6
25. Kim, D. W., Lee, H. J., Kim, M. H., Lee, S.-y., & Kim, T.-y. (2012). Robust sampled-data fuzzy control of nonlinear systems with parametric uncertainties: Its application to depth control of autonomous underwater vehicles. International Journal of Control, Automation and Systems, 10(6), 1164-1172. doi:10.1007/s12555-012-0611-2
26. Kim, H., Kim, D., Park, B., & Lee, S. M. (2020). Artificial Intelligence Vision-Based Monitoring System for Ship Berthing. IEEE Access, 8, 227014-227023. doi:10.1109/ACCESS.2020.3045487
27. Kim, K.-i., & Lee, K. M. (2019). Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors, 19(23). doi:10.3390/s19235273
28. Kim, S. Y., & Moon, B. Y. (2006). Wake distribution prediction on the propeller plane in ship design using artificial intelligence. Ships and Offshore Structures, 1(2), 89-98. doi:10.1533/saos.2006.0113
29. Kontopoulos, I., Varlamis, I., & Tserpes, K. (2021). A distributed framework for extracting maritime traffic patterns. International Journal of Geographical Information Science, 35(4), 767-792. doi:10.1080/13658816.2020.1792914
30. Lee, H.-T., Lee, J.-S., Son, W.-J., & Cho, I.-S. (2020). Development of Machine Learning Strategy for Predicting the Risk Range of Ship's Berthing Velocity. Journal of Marine Science and Engineering, 8(5). doi:10.3390/jmse8050376
31. Lee, H.-T., Lee, J.-S., Yang, H., & Cho, I.-S. (2021). An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm. Applied Sciences, 11(2). doi:10.3390/app11020799
32. Li, H., Bai, J., & Li, Y. (2019). A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput. Physica A: Statistical Mechanics and its Applications, 534, 122025. doi:https://doi.org/10.1016/j.physa.2019.122025
33. Liang, T.-P., & Liu, Y.-H. (2018). Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2-10. doi:https://doi.org/10.1016/j.eswa.2018.05.018
34. Liu, D., & Shi, G. (2020). Ship Collision Risk Assessment Based on Collision Detection Algorithm. IEEE Access, 8, 161969-161980. doi:10.1109/ACCESS.2020.3013957
35. Man, Y., Sturm, T., Lundh, M., & MacKinnon, S. N. (2020). From Ethnographic Research to Big Data Analytics—A Case of Maritime Energy-Efficiency Optimization. Applied Sciences, 10(6). doi:10.3390/app10062134
36. Mekkaoui, S. E., Benabbou, L., & Berrado, A. (2020, 28-30 Oct. 2020). A Systematic Literature Review of Machine Learning Applications for Port's Operations. Paper presented at the 2020 5th International Conference on Logistics Operations Management (GOL).
37. Millington, I., & Funge, J. (2009). Artificial intelligence for games: CRC Press.
38. Munim, Z. H. (2019). Autonomous ships: a review, innovative applications and future maritime business models. Supply Chain Forum: An International Journal, 20(4), 266-279. doi:10.1080/16258312.2019.1631714
39. Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions. Maritime Policy & Management, 47(5), 577-597. doi:10.1080/03088839.2020.1788731
40. Murray, B., & Perera, L. P. (2020). A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data. Ocean Engineering, 209, 107478. doi:https://doi.org/10.1016/j.oceaneng.2020.107478
41. Ozturk, U., Birbil, S. I., & Cicek, K. (2019). Evaluating navigational risk of port approach manoeuvrings with expert assessments and machine learning. Ocean Engineering, 192, 106558. doi:https://doi.org/10.1016/j.oceaneng.2019.106558
42. Peng, Y., Liu, H., Li, X., Huang, J., & Wang, W. (2020). Machine learning method for energy consumption prediction of ships in port considering green ports. Journal of Cleaner Production, 264, 121564. doi:https://doi.org/10.1016/j.jclepro.2020.121564
43. Qiang, L., & Bi-Guang, H. (2020). Artificial Neural Network Controller for Automatic Ship Berthing Using Separate Route. Journal of Web Engineering, 1089-1116.
44. Ruiz-Aguilar, J. J., Moscoso-López, J. A., Urda, D., González-Enrique, J., & Turias, I. (2020). A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities. Applied Sciences, 10(23). doi:10.3390/app10238326
45. Ruiz-Aguilar, J. J., Urda, D., Moscoso-López, J. A., González-Enrique, J., & Turias, I. J. (2020). A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models. Neurocomputing, 391, 282-291. doi:https://doi.org/10.1016/j.neucom.2019.06.109
46. Sanchez-Gonzalez, P.-L., Díaz-Gutiérrez, D., Leo, T. J., & Núñez-Rivas, L. R. (2019). Toward Digitalization of Maritime Transport? Sensors, 19(4). doi:10.3390/s19040926
47. Sanders, D. A. (2009). Recognizing shipbuilding parts using artificial neural networks and Fourier descriptors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(3), 337-342. doi:10.1243/09544054JEM1382
48. Santipantakis, G. M., Glenis, A., Patroumpas, K., Vlachou, A., Doulkeridis, C., Vouros, G. A., . . . Theodoridis, Y. (2020). SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources. Future Generation Computer Systems, 110, 540-555. doi:https://doi.org/10.1016/j.future.2018.07.007
49. Shankar, S., Ilavarasan, P. V., Punia, S., & Singh, S. P. (2020). Forecasting container throughput with long short-term memory networks. Industrial Management & Data Systems, 120(3), 425-441. doi:10.1108/IMDS-07-2019-0370
50. Shin, Y. W., Abebe, M., Noh, Y., Lee, S., Lee, I., Kim, D., . . . Kim, K. C. (2020). Near-Optimal Weather Routing by Using Improved A* Algorithm. Applied Sciences, 10(17). doi:10.3390/app10176010
51. Sirimanne, S. N., Hoffman, J., Juan, W., Asariotis, R., Assaf, M., Ayala, G., . . . Premti, A. (2019). Review of maritime transport 2019. Song, R., Huang, L., Cui, W., Óskarsdóttir, M., & Vanthienen, J. (2020). Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models. Applied Sciences, 10(3). doi:10.3390/app10031056
52. Štepec, D., Martinčič, T., Klein, F., Vladušič, D., & Costa, J. P. (2020, 30 June-3 July 2020). Machine Learning based System for Vessel Turnaround Time Prediction. Paper presented at the 2020 21st IEEE International Conference on Mobile Data Management (MDM).
53. Suo, Y., Chen, W., Claramunt, C., & Yang, S. (2020). A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network. Sensors, 20(18). doi:10.3390/s20185133
54. Tsaganos, G., Nikitakos, N., Dalaklis, D., Ölcer, A. I., & Papachristos, D. (2020). Machine learning algorithms in shipping: improving engine fault detection and diagnosis via ensemble methods. WMU Journal of Maritime Affairs, 19(1), 51-72. doi:10.1007/s13437-019-00192-w
55. Tsou, M.-C. (2018). Big data analytics of safety assessment for a port of entry: A case study in Keelung Harbor. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 233(4), 1260-1275. doi:10.1177/1475090218805245
56. Varlamis, I., Kontopoulos, I., Tserpes, K., Etemad, M., Soares, A., & Matwin, S. (2021). Building navigation networks from multi-vessel trajectory data. GeoInformatica, 25(1), 69-97. doi:10.1007/s10707-020-00421-y
57. Wang, L., Li, Y., Wan, Z., Yang, Z., Wang, T., Guan, K., & Fu, L. (2020). Use of AIS data for performance evaluation of ship traffic with speed control. Ocean Engineering, 204, 107259. doi:https://doi.org/10.1016/j.oceaneng.2020.107259
58. Wang, X., Li, J., & Zhang, T. (2019). A Machine-Learning Model for Zonal Ship Flow Prediction Using AIS Data: A Case Study in the South Atlantic States Region. Journal of Marine Science and Engineering, 7(12). doi:10.3390/jmse7120463
59. Wang, Y., Zhang, F., Yang, Z., & Yang, Z. (2021). Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection. Reliability Engineering & System Safety, 206, 107277. doi:https://doi.org/10.1016/j.ress.2020.107277
60. Wen, Y., Sui, Z., Zhou, C., Xiao, C., Chen, Q., Han, D., & Zhang, Y. (2020). Automatic ship route design between two ports: A data-driven method. Applied Ocean Research, 96, 102049. doi:https://doi.org/10.1016/j.apor.2019.102049
61. Xiao, Z., Fu, X., Zhang, L., & Goh, R. S. M. (2020). Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey. IEEE Transactions on Intelligent Transportation Systems, 21(5), 1796-1825. doi:10.1109/TITS.2019.2908191
62. Xu, G., Chen, C.-H., Li, F., & Qiu, X. (2020). AIS data analytics for adaptive rotating shift in vessel traffic service. Industrial Management & Data Systems, 120(4), 749-767. doi:10.1108/IMDS-01-2019-0056
63. Yan, R., Wang, S., & Du, Y. (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, 138, 101930. doi:https://doi.org/10.1016/j.tre.2020.101930
64. Yan, X., Wang, K., Yuan, Y., Jiang, X., & Negenborn, R. R. (2018). Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors. Ocean Engineering, 169, 457-468. doi:https://doi.org/10.1016/j.oceaneng.2018.08.050
65. Yang, C.-H., & Chang, P.-Y. (2020). Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM. Mathematics, 8(10). doi:10.3390/math8101784
66. Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X. (2019). How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications. Transport Reviews, 39(6), 755-773. doi:10.1080/01441647.2019.1649315
67. Yang, T., Han, C., Qin, M., & Huang, C. (2020). Learning-Aided Intelligent Cooperative Collision Avoidance Mechanism in Dynamic Vessel Networks. IEEE Transactions on Cognitive Communications and Networking, 6(1), 74-82. doi:10.1109/TCCN.2019.2945790
68. Zhang, R., Bahrami, Z., Wang, T., & Liu, Z. (2021). An Adaptive Deep Learning Framework for Shipping Container Code Localization and Recognition. IEEE Transactions on Instrumentation and Measurement, 70, 1-13. doi:10.1109/TIM.2020.3016108
69. Zhao, Z., He, W., & Ge, S. S. (2014). Adaptive Neural Network Control of a Fully Actuated Marine Surface Vessel With Multiple Output Constraints. IEEE Transactions on Control Systems Technology, 22(4), 1536-1543. doi:10.1109/TCST.2013.2281211
70. Zhong, C., Jiang, Z., Chu, X., & Liu, L. (2019). Inland Ship Trajectory Restoration by Recurrent Neural Network. Journal of Navigation, 72(6), 1359-1377. doi:10.1017/S0373463319000316
71. Zhou, X., Liu, Z., Wang, F., Xie, Y., & Zhang, X. (2020). Using Deep Learning to Forecast Maritime Vessel Flows. Sensors, 20(6). doi:10.3390/s20061761