Abstract
This paper presents a simulation approach of the problem of scheduling customers in a queuing networks using a fuzzy inference system. Usually, in the queuing systems there are rules as round robin, equiprobable, shortest queue, among others, to schedule customers, however the condition of the system like the cycle time, utilization and the length of queue is difficult to measure. We propose a fuzzy inference system in order to determine the status in the system using input variables like the length queue and utilization. Our simulation shows an improvement in the performance measures compared with traditional scheduling policies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lopez-Santana, E.R., Méndez-Giraldo, G.A., Florez Becerra, G.F.: On the conceptual design of multi-agent system for load balancing using multi-class queueing networks. In: 2015 Workshop on Engineering Applications - International Congress on Engineering (WEA), pp. 1–7 (2015)
López-Santana, E.R., Franco, C., Figueroa-García, J.C.: A fuzzy inference system to scheduling tasks queueing systems. In: Huang, D.-S., Hussain, A. (eds.) Intelligent Computing Theories and Application. pp. 286–297. Springer, Heidelberg (2017)
Hopp, W.J., Spearman, M.L.: Factory Physics - Foundations of Manufacturing Management. Irwin/McGraw-Hill, New York (2011)
Ross, S.: Introduction to Probability Models. Academic Press, London (2006)
Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. McGraw-Hill Higher Education, Boston (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)
Cruz, F.R.B.: Optimizing the throughput, service rate, and buffer allocation in finite queueing networks. Electron. Notes Discret. Math. 35, 163–168 (2009)
Yang, F., Liu, J.: Simulation-based transfer function modeling for transient analysis of general queueing systems. Eur. J. Oper. Res. 223, 150–166 (2012)
Camastra, F., Ciaramella, A., Giovannelli, V., Lener, M., Rastelli, V., Staiano, A., Staiano, G., Starace, A.: A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Expert Syst. Appl. 42, 1710–1716 (2015)
Alavi, N.: Quality determination of Mozafati dates using Mamdani fuzzy inference system. J. Saudi Soc. Agric. Sci. 12, 137–142 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
López-Santana, E.R., Franco-Franco, C., Figueroa-García, J.C. (2017). Simulation of Fuzzy Inference System to Task Scheduling in Queueing Networks. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-66963-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66962-5
Online ISBN: 978-3-319-66963-2
eBook Packages: Computer ScienceComputer Science (R0)