Work

Investigation of Tandem Queuing Systems Using Machine Learning Methods

Year: 2024

Type: article

Abstract: This paper considers tandem queuing systems with limited buffer sizes in each phase. The system handles an incoming correlated MAP flow and the service time obeys a PH-distribution. Models of such systems and methods for their investigation are briefly reviewed from the historical perspective. According to the review, the problem statement presented below, the methods proposed for solving this problem, and the corresponding results are novel. An accurate algorithm for calculating the performance characteristics of low-dimensional tandem queuing systems is described, including an estimate of the algorithm’s complexity. An approach using both machine learning and simulation modeling is suggested for the investigation of high-dimensional tandem queuing systems. Numerical analysis results are provided to show the effectiveness of machine learning methods for estimating the performance of tandem queuing systems.

Source: Проблемы управления

DOI: 10.25728/cs.2024.4.2

Authors: Vishnevsky V.M., Larionov A.A., Mukhtarov A.A., Sokolov A.M.

Institutions: Russian Academy of Sciences, V. A. Trapeznikov Institute of Control Sciences

Language: English

Topic: Advanced Queuing Theory Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: bronze

Funder: Russian Science Foundation

Award: G6917870368