Araştırma Makalesi
BibTex RIS Kaynak Göster

Deniz Lojistiğinde Yapay Zeka Uygulamalarının Etkileri

Yıl 2022, Sayı: 34, 217 - 225, 31.03.2022
https://doi.org/10.31590/ejosat.1079206

Öz

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.

Kaynakça

  • 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
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  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Dornemann, J., Rückert, N., Fischer, K., & Taraz, A. (2020). Artificial intelligence and operations research in maritime logistics.
  • 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
  • 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).
  • 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
  • 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
  • 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
  • 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
  • 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
  • Hoque, X., & Sharma, S. K. (2020). Ensembled deep learning approach for maritime anomaly detection system. In Proceedings of ICETIT 2019 (pp. 862-869): Springer.
  • 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
  • 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
  • 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
  • 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
  • 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
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The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Yıl 2022, Sayı: 34, 217 - 225, 31.03.2022
https://doi.org/10.31590/ejosat.1079206

Öz

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.

Kaynakça

  • 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
  • Adi, T. N., Iskandar, Y. A., & Bae, H. (2020). Interterminal Truck Routing Optimization Using Deep Reinforcement Learning. Sensors, 20(20). doi:10.3390/s20205794
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Dornemann, J., Rückert, N., Fischer, K., & Taraz, A. (2020). Artificial intelligence and operations research in maritime logistics.
  • 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
  • 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).
  • 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
  • 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
  • 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
  • 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
  • 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
  • Hoque, X., & Sharma, S. K. (2020). Ensembled deep learning approach for maritime anomaly detection system. In Proceedings of ICETIT 2019 (pp. 862-869): Springer.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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  • Qiang, L., & Bi-Guang, H. (2020). Artificial Neural Network Controller for Automatic Ship Berthing Using Separate Route. Journal of Web Engineering, 1089-1116.
  • 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
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Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Batin Latif Aylak 0000-0003-0067-1835

Erken Görünüm Tarihi 30 Ocak 2022
Yayımlanma Tarihi 31 Mart 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 34

Kaynak Göster

APA Aylak, B. L. (2022). The Impacts of the Applications of Artificial Intelligence in Maritime Logistics. Avrupa Bilim Ve Teknoloji Dergisi(34), 217-225. https://doi.org/10.31590/ejosat.1079206