Computational nanotechnologyComputational nanotechnologyComputational nanotechnology2313-223X2587-9693YUR-VAK30971610.33693/2313-223X-2025-12-2-37-47QHHQAPSYSTEM ANALYSIS, INFORMATION MANAGEMENT AND PROCESSING, STATISTICSСИСТЕМНЫЙ АНАЛИЗ, УПРАВЛЕНИЕ И ОБРАБОТКА ИНФОРМАЦИИ, СТАТИСТИКАResearch ArticleArtificial intelligence methods for short-term planning in petroleum products realizationМетоды искусственного интеллекта для краткосрочного планирования на примере процесса отпуска нефтепродуктаIgnatyevYuriy V.ИгнатьевЮрий Васильевич
Russian Federation

postgraduate student

аспирант

Yuriy-Ig@yandex.ruAfanasyevGennady I.АфанасьевГеннадий Иванович
Russian Federation

Cand. Sci. (Eng.), Associate Professor; associate professor

кандидат технический наук, доцент; доцент

gaipcs@bmstu.ruBauman Moscow State Technical UniversityМосковский государственный технический университет имени Н.Э. Баумана19082025122374718092025Copyright ©; 2025, Yur-VAKCopyright ©; 2025, Юр-ВАК2025Yur-VAKЮр-ВАКhttps://www.urvak.ru/contacts/https://journals.rcsi.science/2313-223X/article/view/309716

In this article, a critical analytical review of the application of artificial intelligence methods in the field of scheduling theory is presented, exemplified by the constraints of the short-term planning problem in the process of petroleum products realization via road transport. The objective of the research was to systematize and evaluate existing approaches to solving planning tasks while considering specific temporal constraints inherent to the petroleum products realization process. During the study, both exact and approximate methods for solving scheduling theory problems were analyzed, including heuristic algorithms and approaches based on artificial neural networks. It was established that existing methods have significant limitations when addressing semi-online planning tasks. The research findings demonstrate the necessity for developing a new method capable of promptly restructuring schedules in response to unpredictable changes that arise during the petroleum products realization process. The results of the study highlight the promising potential for advancing artificial intelligence methods to address short-term planning challenges.

В статье представлен критический аналитический обзор применения методов искусственного интеллекта в области теории расписаний, проведенный на примере ограничений проблемы краткосрочного планирования в процессе отпуска нефтепродуктов с нефтебаз автомобильным транспортом. Цель исследования заключалась в систематизации и оценке существующих подходов к решению задач планирования с учетом специфических временных ограничений, к которым относится процесс отпуска нефтепродукта. В ходе исследования проанализированы точные и приближенные методы решения задач теории расписаний, включая эвристические алгоритмы и подходы на основе искусственных нейронных сетей. Установлено, что существующие методы имеют существенные ограничения при решении задач полу-онлайн планирования. Результаты исследования демонстрируют необходимость разработки нового метода, способного оперативно перестраивать расписания с учетом непрогнозируемых изменений, возникающих в ходе процесса отпуска нефтепродукта. Результаты исследования демонстрируют перспективность развития методов искусственного интеллекта для решения задач краткосрочного планирования.

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