student from 01.01.2024 until now
Izhevsk, Izhevsk, Russian Federation
student from 01.01.2024 until now
This paper investigates a two-channel queueing system without a buffer applied to two equivalent service systems. System 1 operates according to a classical scheme with a constant service time, whereas System 2 is equipped with a cyber-physical controller (CPS) that adapts a controllable service parameter u based on the current number of jobs n in the system. The adaptation law is defined as u(n) = min{u0·(1 + β·n); umax}, and the service time is represented as the sum of constant and variable components: T(n) = Tconst + Tvar/(1 + β·n). Baseline parameters are specified as a model calculation scenario sufficient for comparing two control policies without reference to a specific physical object. The comparative analysis was performed by discrete-event simulation in Python using the SimPy library. The experiment included 50 independent runs for each of 30 scenarios with λ ∈ {55, 60, 65, 70, 75, 80} requests/h and β ∈ {0.05, 0.10, 0.15, 0.20, 0.25}, totalling 1,500 runs. The results show that the adaptive system outperforms the classical one in all investigated scenarios: throughput gain ranges from 1.1% under low load to 5.8% under high load. The greatest effect is observed at higher load levels, where the probability of input-flow blocking increases. The optimal zone β ∈ [0.15; 0.20] at λ ≥ 70 requests/h yields a 3.5-4.8% gain while satisfying the specified model constraints. These results confirm the applicability of state-dependent control for increasing the throughput of two-channel systems without changing their structure.
INDUSTRY 4.0, QUEUEING SYSTEM, QUEUEING SYSTEM WITHOUT BUFFER, ADAPTIVE CONTROL, CONTROLLABLE SERVICE PARAMETER, CPS CONTROLLER, SIMULATION MODELING, SIMPY, THROUGHPUT, ACCELERATION COEFFICIENT β
1. H. Kagermann, W. Wahlster, J. Helbig, Recommendations for implementing the strategic initiative INDUSTRIE 4.0, acatech, Frankfurt am Main, 2013.
2. F. Tao, Q. Qi, L. Wang, A.Y.C. Nee, Engineering, 5, 4, 653–661 (2019). DOI: https://doi.org/10.1016/j.eng.2019.01.014.
3. D.A. Barua, S.A. Sami, L. Barua, Scientific Reports, 15, Article 41559 (2025). DOI: https://doi.org/10.1038/s41598-025-25413-6.
4. M.A. Pleskunov, Queueing Theory, Ural University Press, Ekaterinburg, 2022.
5. T.V. Cherusheva, N.V. Zverovshchikova, Queueing Theory, PSU Publishing House, Penza, 2021.
6. A.M. Law, Simulation Modeling and Analysis, 5th ed., McGraw-Hill Education, New York, 2015.
7. M. Lefebvre, Axioms, 14, 2, Article 130 (2025). DOI: https://doi.org/10.3390/axioms14020130.
8. I.N. Valeev, A.P. Kirpichnikov, Herald of Kazan Technol. Univ., 4, 75–79 (2006).
9. N. Matloff, Introduction to Discrete-Event Simulation and the SimPy Language, University of California, Davis, 2008.
10. Y. Zhang, Y. Kurkure, Y. Tao, M.E. Papka, Z. Lan, A RealTime Digital Twin for Adaptive Scheduling, arXiv:2512.18894 (2025).
11. W.J. Hopp, M.L. Spearman, Factory Physics, McGrawHill, New York, 2008.
12. J. Xu, E. Huang, L. Hsieh, L.H. Lee, Q.S. Jia, C.H. Chen, Journal of Simulation, 10, 4, 310–320 (2016). DOI: https://doi.org/10.1057/s41273-016-0037-6.
13. D. Gross, J.F. Shortle, J.M. Thompson, C.M. Harris, Fundamentals of Queueing Theory, Wiley, Hoboken, 2008.
14. I.Z. Samerkhanov, E.A. Pechenyy, N.K. Nuriev, Modern High Technologies, 3, 87–92 (2024). DOI: https://doi.org/10.17513/snt.39951.
15. G. Chen, Z. Liu, L. Xia, Journal of the Operational Research Society, 74, 3, 979–991 (2023). DOI: https://doi.org/10.1080/01605682.2022.2053308.
