70188

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Доклад

Название: 

Application of Machine Learning Methods to Solving Problems of Queuing Theory

ISBN/ISSN: 

978-3-031-09331-9

DOI: 

10.1007/978-3-031-09331-9_24

Наименование конференции: 

  • 20th International Conference on Information Technologies and Mathematical Modelling - Queueing Theory and Applications (ITMM 2021)

Наименование источника: 

  • Proceedings of the 20th International Conference on Information Technologies and Mathematical Modelling - Queueing Theory and Applications (ITMM 2021)

Обозначение и номер тома: 

Vol. 1605

Город: 

  • Cham

Издательство: 

  • Springer

Год издания: 

2022

Страницы: 

304-316
Аннотация
This review is the first to propose the systematic presentation of a new approach to the study of queuing systems and networks. The concept of the new approach is based on a combination of traditional methods of queuing theory with various machine learning algorithms. The detailed description and justification of the possibility of applying the approach are given on the example of a combination of simulation with artificial neural networks. The analysis of publications allows us to conclude that the application of machine learning methods is highly effective, promising for further research, as well as for the possible separation of this new approach into an independent direction in the field of solving complex problems of the theory of queues.

Библиографическая ссылка: 

Вишневский В.М., Горбунова А.В. Application of Machine Learning Methods to Solving Problems of Queuing Theory / Proceedings of the 20th International Conference on Information Technologies and Mathematical Modelling - Queueing Theory and Applications (ITMM 2021). Cham: Springer, 2022. Vol. 1605. С. 304-316.