Abstract
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.
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References
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)
Yang, F.: Neural network metamodeling for cycle time-throughput profiles in manufacturing. Eur. J. Oper. Res. 205, 172–185 (2010). https://doi.org/10.1016/j.ejor.2009.12.026
Hopp, W.J., Spearman, M.L.: Factory Physics—Foundations of Manufacturing Management. Irwin/McGraw-Hill (2011)
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)
Ross, S.: Introduction to Probability Models. Academic Press (2006)
Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. McGraw-Hill Higher Education (2010)
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). https://doi.org/10.1214/aoms/1177728975
Little, J.D.C.: A proof for the queuing formula: L = λ W. Oper. Res. 9, 383–387 (1961). https://doi.org/10.1287/opre.9.3.383
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)
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)
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). https://doi.org/10.1016/j.sorms.2014.09.001
Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer (2009)
López-Santana, E.: Review of scheduling problems in service systems (2018)
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). https://doi.org/10.1016/S0164-1212(02)00068-7
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). https://doi.org/10.1016/j.apm.2007.02.031
Cruz, F.R.B.: Optimizing the throughput, service rate, and buffer allocation in finite queueing networks. Electron. Notes Discret. Math. 35, 163–168 (2009). https://doi.org/10.1016/j.endm.2009.11.028
Yang, F., Liu, J.: Simulation-based transfer function modeling for transient analysis of general queueing systems. Eur. J. Oper. Res. 223, 150–166 (2012). https://doi.org/10.1016/j.ejor.2012.05.040
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). https://doi.org/10.1007/s10586-017-1595-8
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). https://doi.org/10.1007/s11390-013-1336-2
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)
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)
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)
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). https://doi.org/10.1016/j.matcom.2011.06.009
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). https://doi.org/10.1016/j.neucom.2007.02.015
Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993). https://doi.org/10.1109/21.256541
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)
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López-Santana, E., Méndez-Giraldo, G., Figueroa-García, J.C. (2019). Scheduling in Queueing Systems and Networks Using ANFIS. In: Bello, R., Falcon, R., Verdegay, J. (eds) Uncertainty Management with Fuzzy and Rough Sets. Studies in Fuzziness and Soft Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-10463-4_18
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