Модели и методы машинного обучения для решения задач оптимизации и прогнозирования работы морских портов
https://doi.org/10.37661/1816-0301-2022-19-4-94-110
Аннотация
За последнее десятилетие существенно улучшились методы машинного обучения и расширилась сфера их применения, которая дополнилась рядом операционных задач, возникающих в грузовых портах. Это связано с накоплением и возможностью использования имеющихся в грузовых портах больших объемов данных. Статья посвящена обзору литературы по моделям и методам машинного обучения и их применению к оптимизации портовых операций. Основное внимание уделено планированию и развитию портов, их безопасности и охране, водным и сухопутным портовым операциям.
Об авторах
М. Н. Лукашевич
Белорусский государственный университет
Беларусь
Беларусь
Лукашевич Михаил Николаевич, аспирант, факультет прикладной математики и информатики
пр. Независимости, 4, Минск, 220050
М. Я. Ковалев
Объединенный институт проблем информатики Национальной академии наук Беларуси
Беларусь
Беларусь
Ковалев Михаил Яковлевич, член-корреспондент НАН Беларуси
ул. Сурганова, 6, Минск, 220012
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Рецензия
Для цитирования:
Лукашевич М.Н., Ковалев М.Я. Модели и методы машинного обучения для решения задач оптимизации и прогнозирования работы морских портов. Информатика. 2022;19(4):94-110. https://doi.org/10.37661/1816-0301-2022-19-4-94-110
For citation:
Lukashevich M.N., Kovalyov M.Y. Machine learning models and methods for solving optimization and forecasting problems of the work of seaports. Informatics. 2022;19(4):94-110. (In Russ.) https://doi.org/10.37661/1816-0301-2022-19-4-94-110
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ISSN 1816-0301 (Print)
ISSN 2617-6963 (Online)
ISSN 2617-6963 (Online)
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