Skip to main content
SpringerLink
Log in

Estimating the Parameters of Queuing Systems with Channel Switching

  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

We consider the problem of estimating the queuing system structure and parameters when implementing the system of optimal switching of the main service channels at control times separated from each other by a fixed time step. Expert classification analysis, structural forecasting, and expert statistical data processing procedures are proposed to solve the problem of assessing the model structure and parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Cluster centers are sometimes referred to as the standards of a cluster.

  2. Note that these probabilities do not coincide with the desired probabilities in Algorithm 2 (see Sec. 4.3 below).

REFERENCES

  1. Mandel, A. and Laptin, V., Channel switching threshold strategies for multichannel controllable queuing systems, in Commun. Comput. Inf. Sci. Vol. 1337 , Heidelberg: Springer, 2020, pp. 259–270.

  2. Bauman, E.V. and Dorofeyuk, A.A., Classification data analysis, Tr. Mezhdunar. konf. po problemam upravleniya (Proc. Int. Conf. Control Probl.), Moscow: SINTEG, 1999, vol. 1, pp. 62–77.

  3. Dorofeyuk, A.A., Methodology of expert-classification analysis in problems of management and processing of complex data (history and development prospects), Probl. Upr., 2009, no. 3.1, pp. 19–28.

  4. Dorofeyuk, Yu.A. and Chernyavskii, A.L, Intelligent methods for dynamic structural data analysis, Datchiki Sist., 2019, no. 10, pp. 3–8.

  5. Mandel, A.S. and Dorofeyuk, Yu.A., Ekspertno-klassifikatsionnaya i ekspertno-statisticheskaya obrabotka informatsii i prinyatie reshenii (Expert-Classification and Expert-Statistical Information Processing and Decision-Making), Moscow: Maks Press, 2010, pp. 165–168.

    Google Scholar 

  6. Vorontsov, K.V., Ten open problems of probabilistic topic modeling, Intellektualizatsiya obrabotki informatsii: Plenarnyi doklad na 13-y Mezhdunar. konf. IOI-2020 (Intellectualization of Information Processing: Plenary Talk at the 13th Int. Conf. IIP-2020).

  7. Dorofeyuk, A.A., Pokrovskaya, I.V., and Chernyavskii, A.L., Structuring objects of nonnumerical nature, Inf. Tekhnol. Vychisl. Sist., 2018, no. 1, pp. 16–21.

  8. Dorofeyuk, A.A., Pokrovskaya, I.V., and Chernyavsky, A.L., Expert methods to analyze and perfect management systems, Autom. Remote Control, 2004, vol. 65, no. 10, pp. 1675–1688.

    Article  Google Scholar 

  9. Dorofeyuk, A.A., Gol’dovskaya, M.D., Kiseleva, N.E., et al., Procedures for collective multivariate examination in the problems of analyzing and improving socio-economic systems, Inf. Tekhnol. Vychisl. Sist., 2016, no. 4, pp. 53–68.

  10. Troyanovskii, V.M., Programmnaya inzheneriya informatsionno-upravlyayushchikh sistem v svete prikladnoi teorii sluchainykh protsessov (Software Engineering of Information Control Systems in the Light of the Applied Theory of Stochastic Processes), Moscow: Forum, 2019.

    Google Scholar 

  11. Aizerman, M.A., Braverman, E.M., and Rozonoer, L.I., Metod potentsial’nykh funktsii v teorii obucheniya mashin (Potential Function Method in Machine Learning Theory), Moscow: Nauka, 1970.

    Google Scholar 

  12. Shibzukhov, Z.M., On one robust approach to finding cluster centers, Intellektualizatsiya obrabotki informatsii: Tezisy dokladov 13-i Mezhdunar. konf. IOI-2020, (Intellectualization of Information Processing: Abstr. Rep. 13th Int. Conf. IIP-2020), Moscow: Ross. Akad. Nauk, 2020, pp. 121–122.

  13. Haykin, S., Neural Networks. A Comprehensive Foundation, Pearson, 1999. Translated under the title: Neironnye seti. Polnyi kurs, Moscow–St. Petersburg: Dialektika, 2020.

Download references

Author information

Authors and Affiliations

  1. Lomonosov Moscow State University, Moscow, 119991, Russia

    Yu. A. Dorofeyuk & V. A. Laptin

  2. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, 117997, Russia

    A. S. Mandel

Authors
  1. Yu. A. Dorofeyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. A. Laptin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. S. Mandel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yu. A. Dorofeyuk, V. A. Laptin or A. S. Mandel.

Additional information

Translated by V. Potapchouck

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dorofeyuk, Y.A., Laptin, V.A. & Mandel, A.S. Estimating the Parameters of Queuing Systems with Channel Switching. Autom Remote Control 82, 1886–1891 (2021). https://doi.org/10.1134/S0005117921110047

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117921110047

Keywords

Search

Navigation