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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

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

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)