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

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
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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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