Application of algorithmic models of machine learning to the freight transportation process

Simple item page
dc.citation.epage21
dc.citation.issue2
dc.citation.spage10
dc.contributor.affiliationVinnytsia National Technical University
dc.contributor.authorKotenko, Viktoriia
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-02-09T14:47:28Z
dc.date.available2023-02-09T14:47:28Z
dc.date.created2022-03-01
dc.date.issued2022-03-01
dc.description.abstractУ роботі наведено результати аналізу застосування алгоритмічних моделей машинного навчання до процесу перевезення вантажів. Аналіз існуючих досліджень дозволив виявити ряд переваг застосування обчислювального інтелекту у логістичних системах, серед яких: підвищення точності прогнозування, зменшення транспортних витрат, підвищення ефективності доставки вантажів, зниження ризиків, пошук ключових факторів ефективності. У процесі дослідження було визначено основні напрями застосування алгоритмічних моделей машинного навчання, як-от: маршрутизація транспортних засобів, вибір виду вантажу, виду транспортування та типу транспортних засобів; прогнозування витрат палива транспортними засобами, збоїв у транспортуванні, транспортних витрат, тривалості виконання замовлення; оцінка парку рухомого складу та ефективності виконання транспортного завдання. На основі досліджуваних публікацій було виявлено найбільш поширені у вантажних перевезеннях алгоритмічні моделі машинного навчання та проаналізовано їхню ефективність. Моделі лінійної та логістичної регресії є достатньо простими, проте не завжди дають високі показники моделювання; моделі глибокого навчання досить широко застосовуються до всіх виявлених напрямів; моделі дерев рішень та випадкового лісу часто показують найвищі показники ефективності моделювання; моделі k-найближчих сусідів та опорних векторів доцільно застосовувати як у задачах класифікації, наприклад, вибору виду вантажу та виду транспортування, так і для прогнозування витрат палива та тривалості транспортного процесу.
dc.description.abstractThe results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process.
dc.format.extent10-21
dc.format.pages12
dc.identifier.citationKotenko V. Application of algorithmic models of machine learning to the freight transportation process / Viktoriia Kotenko // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 3. — No 2. — P. 10–21.
dc.identifier.citationenKotenko V. Application of algorithmic models of machine learning to the freight transportation process / Viktoriia Kotenko // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 3. — No 2. — P. 10–21.
dc.identifier.doidoi.org/10.23939/tt2022.02.010
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/57316
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofTransport Technologies, 2 (3), 2022
dc.relation.references1. Mitchell, Tom. (1997). Machine Learning. New York: McGraw-Hill. (in English).
dc.relation.references2. Abdelwahab, W., & Sayed, T. (1999). Freight mode choice models using artificial neural networks. Civil Engineering And Environmental Systems, 16(4), 267–286. doi: 10.1080/02630259908970267 (in English).
dc.relation.references3. Profillidis, V., & Botzoris, G. (2019). Artificial Intelligence-Neural Network Methods. Modeling Of Transport Demand, 353–382. doi: 10.1016/b978-0-12-811513-8.00008-x (in English).
dc.relation.references4. Hryhorov, O. V., Anishchenko H. O., Stryzhak V. V., Petrenko N. O., Turchyn O. V., Okun A. O. & Ponomarov O. E. (2019). Shtuchnyi intelekt. Mashynne navchannia [Artificial Intelligence. Machine learning]. Avtomobil i elektronika. Suchasni tekhnolohii [Vehicle and Electronics. Innovative Technologies]. No. 15. pp. 17–27. (in Ukrainian).
dc.relation.references5. Tsolaki, K., Vafeiadis, T., Nizamis, A., Ioannidis, D., & Tzovaras, D. (2022). Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express. doi: 10.1016/j.icte.2022.02.001 (in English).
dc.relation.references6. Horiainov A. N. (2020). Mashynnoe obuchenye v lohystycheskykh y transportnykh systemakh. [Machine learning in logistics and transport systems]. Ukraina – YeS: problemy naukovoi ta haluzevoi intehratsii: Mater V Vseukr. zaoch. nauk.-pr. konf. Naukove partnerstvo “Tsentr naukovykh tekhnolohii” [Ukraine – EU: problems of scientific and industry integration: Mater V Vseukr. extramural science.-prof. conf. Scientific partnership “Center of Scientific Technologies” ]. P. 34–42 (in Ukrainian).
dc.relation.references7. Liu, C., Shu, T., Chen, S., Wang, S., Lai, K., & Gan, L. (2016). An improved grey neural network model for predicting transportation disruptions. Expert Systems With Applications, 45, 331–340. doi: 10.1016/j.eswa. 2015.09.052 (in English).
dc.relation.references8. Becker, T., Illigen, C., McKelvey, B., Hülsmann, M., & Windt, K. (2016). Using an agent-based neural-network computational model to improve product routing in a logistics facility. International Journal of Production Economics, 174, 156–167 (in English).
dc.relation.references9. Van der Spoel, S., Amrit, C., & van Hillegersberg, J. (2015). Predictive analytics for truck arrival time estimation: a field study at a European distribution centre. International Journal Of Production Research, 55(17), 5062–5078. doi: 10.1080/00207543.2015.1064183 (in English).
dc.relation.references10. AdaBoost Algorithm – A Complete Guide for Beginners. Retrieved from: https://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/ (in English).
dc.relation.references11. Servos, N., Liu, X., Teucke, M., & Freitag, M. (2019). Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms. Logistics, 4(1), 1. doi: 10.3390/logistics4010001 (in English).
dc.relation.references12. Yakushenko A., Shevchuk D., & Medynskyi D. (2021). Neiromerezheva model dlia prohnozuvannia chasuna vykonannia transportnoi zadachi. [Neural network model for predicting the execution time of a transport task] Science-Based Technologies, 49(1), 33–38. doi:10.18372/2310-5461.49.15289 (in Ukrainian).
dc.relation.references13. Tortum, A., Yayla, N., & Gökdağ, M. (2009). The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system. Expert Systems With Applications, 36(3), 6199–6217. doi: 10.1016/j.eswa.2008.07.032 (in English).
dc.relation.references14. Samimi, A., Razi-Ardakani, H., & Nohekhan, A. (2017). A Comparison between Different Data Mining Algorithms in Freight Mode Choice. American Journal Of Applied Sciences, 14(2), 204–216. doi: 10.3844/ajassp.2017.204.216 (in English).
dc.relation.references15. Ahmed, U., & Roorda, M. (2021). Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods. Transportation Research Record: Journal Of The Transportation Research Board, 2676(2), 541–552. doi: 10.1177/03611981211044462 (in English).
dc.relation.references16. Rykała, M., & Rykała, Ł. (2021). Economic Analysis of a Transport Company in the Aspect of Car Vehicle Operation. Sustainability, 13(1), 427. doi: 10.3390/su13010427 (in English).
dc.relation.references17. Bakhtyar, S., & Henesey, L. (2014). Freight transport prediction using electronic waybills and machine learning. Proceedings 2014 International Conference On Informative And Cybernetics For Computational Social Systems (ICCSS), pp. 128–133. doi: 10.1109/iccss.2014.6961829 (in English).
dc.relation.references18. Perrotta, F., Parry, T., & Neves, L. (2017). Application of machine learning for fuel consumption modelling of trucks. 2017 IEEE International Conference On Big Data (Big Data), pp. 3810–3815. doi: 10.1109/bigdata.2017.8258382 (in English).
dc.relation.references19. Schoen, A., Byerly, A., Hendrix, B., Bagwe, R., Santos, E., & Miled, Z. (2019). A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles. IEEE Transactions On Vehicular Technology, 68(7), pp. 6343–6351. doi: 10.1109/tvt.2019.2916299 (in English).
dc.relation.references20. Bousonville, T., Cheubou Kamga, D., Krüger, T., & Dirichs, M. (2020). Data driven analysis and forecasting of medium and heavy truck fuel consumption. Enterprise Information Systems, 16(6), 1–19. doi: 10.1080/17517575.2020.1856417 (in English).
dc.relation.references21. Hamed, M., H.Khafagy, M., & M.Badry, R. (2021). Fuel Consumption Prediction Model using Machine Learning. International Journal Of Advanced Computer Science And Applications, 12(11). doi: 10.14569/ijacsa.2021.0121146 (in English).
dc.relation.references22. Gong, J., Shang, J., Li, L., Zhang, C., He, J., & Ma, J. (2021). A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors. Energies, 14(23), 8106. doi: 10.3390/en14238106 (in English).
dc.relation.references23. Budzyński, A., & Sładkowski, A. (2021). The Use of Machine Learning to Predict Diesel Fuel Consumption in Road Vehicles. 19th European Transport Congress of the EPTS Foundation e.V: European Green Deal Challenges and Solutions for Mobility and Logistics in Cities, pp. 1-6 (in English).
dc.relation.references24. Topić, J., Škugor, B., & Deur, J. (2022). Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. Sustainability, 14(2), 744. doi: 10.3390/su14020744 (in English).
dc.relation.references25. Świderski, A., Jóżwiak, A., & Jachimowski, R. (2018). Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks. Eksploatacja I Niezawodnosc – Maintenance And Reliability, 20(2), 292–299. doi: 10.17531/ein.2018.2.16 (in English).
dc.relation.references26. Singh, A., Das, A., Bera, U., & Lee, G. (2021). Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks. IEEE Access, 9, pp. 103497–103512. doi: 10.1109/access.2021.3098657 (in English).
dc.relation.references27. Kotenko, V.I. (2022). Justification of the feasibility of using artificial neural networks for modeling the transport process of the supply of agricultural products [Obgruntuvannia dotsilnosti zastosuvannia shtuchnykh neironnykh merezh dlia modeliuvannia transportnoho protsesu postachannia silskohospodarskoi produktsii]. X mizhnarodna naukovo-tekhnichna internet-konferentsiia “Problemy i perspektyvy rozvytku avtomobilnoho transportu” [10th International Scientific and Technical Internet Conference “Problems and Prospects of Road Transport Development”]. pp. 172–174. (in Ukrainian).
dc.relation.references28. Salakhutdinov, R. (2015). Learning Deep Generative Models. Annual Review Of Statistics And Its Application, 2(1), 361–385. doi: 10.1146/annurev-statistics-010814-020120 (in English).
dc.relation.references29. Kyslova, O., & Bondarenko, K.B. (2010). Mozhlyvosti zastosuvannia shtuchnykh neironnykh merezh v analizi sotsiolohichnoi informatsii [Possibilities of using artificial neural networks in the analysis of sociological information]. Sotsiolohichni doslidzhennia suchasnoho suspilstva: metodolohiia, teoriia, metody [Sociological research of modern society: methodology, theory, methods], 26. 78–82 (in Ukrainian).
dc.relation.references30. Witten, I. H., Frank, E. & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Amsterdam: Morgan Kaufmann. (in English).
dc.relation.references31. Ni, D., Xiao, Z., & Lim, M. (2019). A systematic review of the research trends of machine learning in supply chain management. International Journal Of Machine Learning And Cybernetics, 11(7), 1463–1482. doi: 10.1007/s13042-019-01050-0
dc.relation.references32. Top 4 advantages and disadvantages of Support Vector Machine or SVM. Retrieved from: https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107 (in English).
dc.relation.references33. Russel, S.J., & Norvig, P. (2003). Artificial Intelligence –- A Modern Approach. Prentice Hall Inc., New Jersey (in English).
dc.relation.references34. Goodfellow, I., Bengio, Y., & Courville A. (2016). Deep Learning. Adaptive Computation and Machine Learning series. Retrieved from: https://mmsjapan.jp/sites/default/files/pdf-deep-learning-adaptive-computation-andmachine-learning-series-ian-goodfellow-yoshua-bengio-aaron-courville-pdf-download-free-book-7fd0d64.pdf (in English).
dc.relation.references35. Jordan, M., & Mitchell, T. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. doi: 10.1126/science.aaa8415 (in English).
dc.relation.references36. Rokach L., & Maimon D. (2015). Data mining with decision trees. Theory and applications (2nd ed). World Scientific Publishing Company. P. 305. (in English).
dc.relation.references37. Breiman, L. Random Forests. Machine Learning, 45, 5–32 (2001). doi: 10.1023/A:1010933404324 (in English).
dc.relation.references38. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9, 381–386 (in English).
dc.relation.references39. Cunningham, P. and Delany, S., 2022. k-Nearest Neighbour Classifiers – A Tutorial. ACM Computing Surveys, 54(6), pp. 1–25. doi: 10.1145/3459665 (in English).
dc.relation.references40. Vapnik, V. The nature of statistical learning theory. Springer, New York, 1995. (in English).
dc.relation.referencesen1. Mitchell, Tom. (1997). Machine Learning. New York: McGraw-Hill. (in English).
dc.relation.referencesen2. Abdelwahab, W., & Sayed, T. (1999). Freight mode choice models using artificial neural networks. Civil Engineering And Environmental Systems, 16(4), 267–286. doi: 10.1080/02630259908970267 (in English).
dc.relation.referencesen3. Profillidis, V., & Botzoris, G. (2019). Artificial Intelligence-Neural Network Methods. Modeling Of Transport Demand, 353–382. doi: 10.1016/b978-0-12-811513-8.00008-x (in English).
dc.relation.referencesen4. Hryhorov, O. V., Anishchenko H. O., Stryzhak V. V., Petrenko N. O., Turchyn O. V., Okun A. O. & Ponomarov O. E. (2019). Shtuchnyi intelekt. Mashynne navchannia [Artificial Intelligence. Machine learning]. Avtomobil i elektronika. Suchasni tekhnolohii [Vehicle and Electronics. Innovative Technologies]. No. 15. pp. 17–27. (in Ukrainian).
dc.relation.referencesen5. Tsolaki, K., Vafeiadis, T., Nizamis, A., Ioannidis, D., & Tzovaras, D. (2022). Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express. doi: 10.1016/j.icte.2022.02.001 (in English).
dc.relation.referencesen6. Horiainov A. N. (2020). Mashynnoe obuchenye v lohystycheskykh y transportnykh systemakh. [Machine learning in logistics and transport systems]. Ukraina – YeS: problemy naukovoi ta haluzevoi intehratsii: Mater V Vseukr. zaoch. nauk.-pr. konf. Naukove partnerstvo "Tsentr naukovykh tekhnolohii" [Ukraine – EU: problems of scientific and industry integration: Mater V Vseukr. extramural science.-prof. conf. Scientific partnership "Center of Scientific Technologies" ]. P. 34–42 (in Ukrainian).
dc.relation.referencesen7. Liu, C., Shu, T., Chen, S., Wang, S., Lai, K., & Gan, L. (2016). An improved grey neural network model for predicting transportation disruptions. Expert Systems With Applications, 45, 331–340. doi: 10.1016/j.eswa. 2015.09.052 (in English).
dc.relation.referencesen8. Becker, T., Illigen, C., McKelvey, B., Hülsmann, M., & Windt, K. (2016). Using an agent-based neural-network computational model to improve product routing in a logistics facility. International Journal of Production Economics, 174, 156–167 (in English).
dc.relation.referencesen9. Van der Spoel, S., Amrit, C., & van Hillegersberg, J. (2015). Predictive analytics for truck arrival time estimation: a field study at a European distribution centre. International Journal Of Production Research, 55(17), 5062–5078. doi: 10.1080/00207543.2015.1064183 (in English).
dc.relation.referencesen10. AdaBoost Algorithm – A Complete Guide for Beginners. Retrieved from: https://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/ (in English).
dc.relation.referencesen11. Servos, N., Liu, X., Teucke, M., & Freitag, M. (2019). Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms. Logistics, 4(1), 1. doi: 10.3390/logistics4010001 (in English).
dc.relation.referencesen12. Yakushenko A., Shevchuk D., & Medynskyi D. (2021). Neiromerezheva model dlia prohnozuvannia chasuna vykonannia transportnoi zadachi. [Neural network model for predicting the execution time of a transport task] Science-Based Technologies, 49(1), 33–38. doi:10.18372/2310-5461.49.15289 (in Ukrainian).
dc.relation.referencesen13. Tortum, A., Yayla, N., & Gökdağ, M. (2009). The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system. Expert Systems With Applications, 36(3), 6199–6217. doi: 10.1016/j.eswa.2008.07.032 (in English).
dc.relation.referencesen14. Samimi, A., Razi-Ardakani, H., & Nohekhan, A. (2017). A Comparison between Different Data Mining Algorithms in Freight Mode Choice. American Journal Of Applied Sciences, 14(2), 204–216. doi: 10.3844/ajassp.2017.204.216 (in English).
dc.relation.referencesen15. Ahmed, U., & Roorda, M. (2021). Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods. Transportation Research Record: Journal Of The Transportation Research Board, 2676(2), 541–552. doi: 10.1177/03611981211044462 (in English).
dc.relation.referencesen16. Rykała, M., & Rykała, Ł. (2021). Economic Analysis of a Transport Company in the Aspect of Car Vehicle Operation. Sustainability, 13(1), 427. doi: 10.3390/su13010427 (in English).
dc.relation.referencesen17. Bakhtyar, S., & Henesey, L. (2014). Freight transport prediction using electronic waybills and machine learning. Proceedings 2014 International Conference On Informative And Cybernetics For Computational Social Systems (ICCSS), pp. 128–133. doi: 10.1109/iccss.2014.6961829 (in English).
dc.relation.referencesen18. Perrotta, F., Parry, T., & Neves, L. (2017). Application of machine learning for fuel consumption modelling of trucks. 2017 IEEE International Conference On Big Data (Big Data), pp. 3810–3815. doi: 10.1109/bigdata.2017.8258382 (in English).
dc.relation.referencesen19. Schoen, A., Byerly, A., Hendrix, B., Bagwe, R., Santos, E., & Miled, Z. (2019). A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles. IEEE Transactions On Vehicular Technology, 68(7), pp. 6343–6351. doi: 10.1109/tvt.2019.2916299 (in English).
dc.relation.referencesen20. Bousonville, T., Cheubou Kamga, D., Krüger, T., & Dirichs, M. (2020). Data driven analysis and forecasting of medium and heavy truck fuel consumption. Enterprise Information Systems, 16(6), 1–19. doi: 10.1080/17517575.2020.1856417 (in English).
dc.relation.referencesen21. Hamed, M., H.Khafagy, M., & M.Badry, R. (2021). Fuel Consumption Prediction Model using Machine Learning. International Journal Of Advanced Computer Science And Applications, 12(11). doi: 10.14569/ijacsa.2021.0121146 (in English).
dc.relation.referencesen22. Gong, J., Shang, J., Li, L., Zhang, C., He, J., & Ma, J. (2021). A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors. Energies, 14(23), 8106. doi: 10.3390/en14238106 (in English).
dc.relation.referencesen23. Budzyński, A., & Sładkowski, A. (2021). The Use of Machine Learning to Predict Diesel Fuel Consumption in Road Vehicles. 19th European Transport Congress of the EPTS Foundation e.V: European Green Deal Challenges and Solutions for Mobility and Logistics in Cities, pp. 1-6 (in English).
dc.relation.referencesen24. Topić, J., Škugor, B., & Deur, J. (2022). Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. Sustainability, 14(2), 744. doi: 10.3390/su14020744 (in English).
dc.relation.referencesen25. Świderski, A., Jóżwiak, A., & Jachimowski, R. (2018). Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks. Eksploatacja I Niezawodnosc – Maintenance And Reliability, 20(2), 292–299. doi: 10.17531/ein.2018.2.16 (in English).
dc.relation.referencesen26. Singh, A., Das, A., Bera, U., & Lee, G. (2021). Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks. IEEE Access, 9, pp. 103497–103512. doi: 10.1109/access.2021.3098657 (in English).
dc.relation.referencesen27. Kotenko, V.I. (2022). Justification of the feasibility of using artificial neural networks for modeling the transport process of the supply of agricultural products [Obgruntuvannia dotsilnosti zastosuvannia shtuchnykh neironnykh merezh dlia modeliuvannia transportnoho protsesu postachannia silskohospodarskoi produktsii]. X mizhnarodna naukovo-tekhnichna internet-konferentsiia "Problemy i perspektyvy rozvytku avtomobilnoho transportu" [10th International Scientific and Technical Internet Conference "Problems and Prospects of Road Transport Development"]. pp. 172–174. (in Ukrainian).
dc.relation.referencesen28. Salakhutdinov, R. (2015). Learning Deep Generative Models. Annual Review Of Statistics And Its Application, 2(1), 361–385. doi: 10.1146/annurev-statistics-010814-020120 (in English).
dc.relation.referencesen29. Kyslova, O., & Bondarenko, K.B. (2010). Mozhlyvosti zastosuvannia shtuchnykh neironnykh merezh v analizi sotsiolohichnoi informatsii [Possibilities of using artificial neural networks in the analysis of sociological information]. Sotsiolohichni doslidzhennia suchasnoho suspilstva: metodolohiia, teoriia, metody [Sociological research of modern society: methodology, theory, methods], 26. 78–82 (in Ukrainian).
dc.relation.referencesen30. Witten, I. H., Frank, E. & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Amsterdam: Morgan Kaufmann. (in English).
dc.relation.referencesen31. Ni, D., Xiao, Z., & Lim, M. (2019). A systematic review of the research trends of machine learning in supply chain management. International Journal Of Machine Learning And Cybernetics, 11(7), 1463–1482. doi: 10.1007/s13042-019-01050-0
dc.relation.referencesen32. Top 4 advantages and disadvantages of Support Vector Machine or SVM. Retrieved from: https://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107 (in English).
dc.relation.referencesen33. Russel, S.J., & Norvig, P. (2003). Artificial Intelligence –- A Modern Approach. Prentice Hall Inc., New Jersey (in English).
dc.relation.referencesen34. Goodfellow, I., Bengio, Y., & Courville A. (2016). Deep Learning. Adaptive Computation and Machine Learning series. Retrieved from: https://mmsjapan.jp/sites/default/files/pdf-deep-learning-adaptive-computation-andmachine-learning-series-ian-goodfellow-yoshua-bengio-aaron-courville-pdf-download-free-book-7fd0d64.pdf (in English).
dc.relation.referencesen35. Jordan, M., & Mitchell, T. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. doi: 10.1126/science.aaa8415 (in English).
dc.relation.referencesen36. Rokach L., & Maimon D. (2015). Data mining with decision trees. Theory and applications (2nd ed). World Scientific Publishing Company. P. 305. (in English).
dc.relation.referencesen37. Breiman, L. Random Forests. Machine Learning, 45, 5–32 (2001). doi: 10.1023/A:1010933404324 (in English).
dc.relation.referencesen38. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9, 381–386 (in English).
dc.relation.referencesen39. Cunningham, P. and Delany, S., 2022. k-Nearest Neighbour Classifiers – A Tutorial. ACM Computing Surveys, 54(6), pp. 1–25. doi: 10.1145/3459665 (in English).
dc.relation.referencesen40. Vapnik, V. The nature of statistical learning theory. Springer, New York, 1995. (in English).
dc.relation.urihttps://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/
dc.relation.urihttps://dhirajkumarblog.medium.com/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107
dc.relation.urihttps://mmsjapan.jp/sites/default/files/pdf-deep-learning-adaptive-computation-andmachine-learning-series-ian-goodfellow-yoshua-bengio-aaron-courville-pdf-download-free-book-7fd0d64.pdf
dc.rights.holder© Національний університет „Львівська політехніка“, 2022
dc.rights.holder© V. Kotenko, 2022
dc.subjectінтелектуальний підхід
dc.subjectмашинне навчання
dc.subjectалгоритмічні моделі машинного навчання
dc.subjectвантажні перевезення
dc.subjectдоставка вантажів
dc.subjectintellectual approach
dc.subjectmachine learning
dc.subjectalgorithmic models of machine learning
dc.subjectfreight transportation
dc.subjectcargo delivery
dc.titleApplication of algorithmic models of machine learning to the freight transportation process
dc.title.alternativeЗастосування алгоритмічних моделей машинного навчання до процесу перевезення вантажів
dc.typeArticle

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Transport Technologies. – 2022. – Vol. 3, No. 2