Scheduling in Queueing Systems and Networks Using ANFIS

  • Eduyn López-SantanaEmail author
  • Germán Méndez-Giraldo
  • Juan Carlos Figueroa-García
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 377)


This paper is concerned with a scheduling problem in many real-world systems where the customers must be waiting for a service known as queueing system. Classical queueing systems are handled using probabilistic theories, mostly based on asymptotic theory and/or samples analysis. We address a situation where neither enough statistical data exists, nor asymptotic behavior can be applied to. This way, we propose to use an Adaptive Neuro-Fuzzy Inference System (ANFIS) method to infer scheduling rules of a queueing problem, based on uncertain data. We use the utilization ratio and the work in process (WIP) of a queue to train an ANFIS network to finally obtain the estimated cycle time of all tasks. Multiple tasks and rework are considered into the problem, so it cannot be easily modeled using classical probability theory. The experiment results through simulation analysis show an improvement of our ANFIS method in the performance measures compared with traditional scheduling policies.


ANFIS Scheduling Queueing systems Queueing networks Utilization WIP 


  1. 1.
    López-Santana, E.R., Franco, C., Figueroa-Garcia, J.C.: A Fuzzy inference system to scheduling tasks in queueing systems. In: Huang, D.-S., Hussain, A., Han, K., Gromiha, M.M. (eds.) Intelligent Computing Methodologies, pp. 286–297. Springer International Publishing AG (2017)Google Scholar
  2. 2.
    Yang, F.: Neural network metamodeling for cycle time-throughput profiles in manufacturing. Eur. J. Oper. Res. 205, 172–185 (2010). Scholar
  3. 3.
    Hopp, W.J., Spearman, M.L.: Factory Physics—Foundations of Manufacturing Management. Irwin/McGraw-Hill (2011)Google Scholar
  4. 4.
    Lopez-Santana, E., Mendez-Giraldo, G., Figueroa-García, J.C.: An ANFIS-based approach to scheduling in queueing systems. In: 2nd International Symposium on Fuzzy and Rough Sets (ISFUROS 2017), pp. 1–12. Santa Clara, Cuba (2017)Google Scholar
  5. 5.
    Ross, S.: Introduction to Probability Models. Academic Press (2006)Google Scholar
  6. 6.
    Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. McGraw-Hill Higher Education (2010)Google Scholar
  7. 7.
    Kendall, D.G.: Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov Chain. Ann. Math. Stat. 24, 338–354 (1953). Scholar
  8. 8.
    Little, J.D.C.: A proof for the queuing formula: L = λ W. Oper. Res. 9, 383–387 (1961). Scholar
  9. 9.
    Little, J.D.C., Graves, S.C.: Little’s law. In: Chhajed, D., Lowe, T.J. (eds.) Building Intuition: Insights From Basic Operations Management Models and Principles, pp. 81–100. Springer, Boston, MA (2008)CrossRefGoogle Scholar
  10. 10.
    López-Santana, E.R., Méndez-Giraldo, G.A.: A knowledge-based expert system for scheduling in services systems. In: Figueroa-García, J.C., López-Santana, E.R., Ferro-Escobar, R. (eds.) Applied Computer Sciences in Engineering WEA 2016, pp. 212–224. Springer International Publishing AG (2016)Google Scholar
  11. 11.
    Terekhov, D., Down, D.G., Beck, J.C.: Queueing-theoretic approaches for dynamic scheduling: a survey. Surv. Oper. Res. Manag. Sci. 19, 105–129 (2014). Scholar
  12. 12.
    Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer (2009)Google Scholar
  13. 13.
    López-Santana, E.: Review of scheduling problems in service systems (2018)Google Scholar
  14. 14.
    Baldwin, R.O., Davis IV, N.J., Midkiff, S.F., Kobza, J.E.: Queueing network analysis: concepts, terminology, and methods. J. Syst. Softw. 66, 99–117 (2003). Scholar
  15. 15.
    Jain, M., Maheshwari, S., Baghel, K.P.S.: Queueing network modelling of flexible manufacturing system using mean value analysis. Appl. Math. Model. 32, 700–711 (2008). Scholar
  16. 16.
    Cruz, F.R.B.: Optimizing the throughput, service rate, and buffer allocation in finite queueing networks. Electron. Notes Discret. Math. 35, 163–168 (2009). Scholar
  17. 17.
    Yang, F., Liu, J.: Simulation-based transfer function modeling for transient analysis of general queueing systems. Eur. J. Oper. Res. 223, 150–166 (2012). Scholar
  18. 18.
    Suganthi, N., Meenakshi, S.: An efficient scheduling algorithm using queuing system to minimize starvation of non-real-time secondary users in cognitive radio network. Clust. Comput. 1–11 (2018).
  19. 19.
    Chude-Olisah, C.C., Chude-Okonkwo, U.A.K., Bakar, K.A., Sulong, G.: Fuzzy-based dynamic distributed queue scheduling for packet switched networks. J. Comput. Sci. Technol. 28, 357–365 (2013). Scholar
  20. 20.
    Cho, H.C., Fadali, M.S., Hyunjeong L.: Dynamic queue scheduling using fuzzy systems for internet routers. In: The 14th IEEE International Conference on Fuzzy Systems, FUZZ’05, pp. 471–476. IEEE (2005)Google Scholar
  21. 21.
    Cho, H.C., Fadali, M.S., Lee, J.W., Lee, Y.J., Lee, K.S.: Lyapunov-based fuzzy queue scheduling for internet routers TT. Int. J. Control Autom. Syst. 5, 317–323 (2007)Google Scholar
  22. 22.
    López-Santana, E.R., Franco-Franco, C., Figueroa-García, J.C.: Simulation of fuzzy inference system to task scheduling in queueing networks. In: Communications in Computer and Information Science, pp. 263–274 (2017)Google Scholar
  23. 23.
    Azadeh, A., Faiz, Z.S., Asadzadeh, S.M., Tavakkoli-Moghaddam, R.: An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems. Math. Comput. Simul. 82, 666–678 (2011). Scholar
  24. 24.
    Geethanjali, M., Raja Slochanal, S.M.: A combined adaptive network and fuzzy inference system (ANFIS) approach for overcurrent relay system. Neurocomputing 71, 895–903 (2008). Scholar
  25. 25.
    Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993). Scholar
  26. 26.
    López-Santana, E.R., Méndez-Giraldo, G.A.: A non-linear optimization model and ANFIS-based approach to knowledge acquisition to classify service systems. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) Intelligent Computing Theories and Application, pp. 789–801. Springer International Publishing (2016)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eduyn López-Santana
    • 1
    Email author
  • Germán Méndez-Giraldo
    • 1
  • Juan Carlos Figueroa-García
    • 1
  1. 1.Universidad Distrital Francisco José de CaldasBogotáColombia

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