Abstract
This paper studies the problem of scheduling customers or tasks in a queuing system. Generally the customers or a set of tasks in queuing system are attended according with different rules as round robin, equiprobable, shortest queue, among others. However, the condition of the system like the work in process, utilization and the length of queue is difficult to measure. We propose to use a fuzzy inference system in order to determine the status in the system depended of input variables like the length queue and the utilization. The experiment results shows an improvement in the performance measures compared with traditional scheduling policies.
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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)
Cruz, F.R.B., Kendall, G., While, L., Duarte, A.R., Brito, N.L.C.: Throughput maximization of queueing networks with simultaneous minimization of service rates and buffers. Math. Probl. Eng. 2012, 1–19 (2012)
Yang, F.: Neural network metamodeling for cycle time-throughput profiles in manufacturing. Eur. J. Oper. Res. 205, 172–185 (2010)
Hopp, W.J., Spearman, M.L.: Factory Physics - Foundations of Manufacturing Management. Irwin/McGraw-Hill, New York (2011)
Rabta, B., Schodl, R., Reiner, G., Fichtinger, J.: A hybrid analysis method for multi-class queueing networks with multi-server nodes. Decis. Support Syst. 54, 1541–1547 (2013)
Negi, D.S., Lee, E.S.: Analysis and simulation of fuzzy queues. Fuzzy Sets Syst. 46, 321–330 (1992)
Zhang, H., Tam, C.M., Li, H.: Modeling uncertain activity duration by fuzzy number and discrete-event simulation. Eur. J. Oper. Res. 164, 715–729 (2005)
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.) WEA 2016. CCIS, vol. 657, pp. 212–224. Springer, Cham (2016). doi:10.1007/978-3-319-50880-1_19
Rojek, I., Jagodziński, M.: Hybrid artificial intelligence system in constraint based scheduling of integrated manufacturing ERP systems. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) Hybrid Artificial Intelligent Systems, pp. 229–240. Springer, Berlin Heidelberg (2012)
Álvarez, L., Caicedo, C., Malaver, M., Méndez, G.: Design of system expert prototype to scheduling in job-shop environment. Revista Científica 12, 125–136 (2010). (in Spanish)
Pereira, I., Madureira, A.: Self-optimization module for scheduling using case-based reasoning. Appl. Soft Comput. 13, 1419–1432 (2013)
Madureira, A., Pereira, I., Pereira, P., Abraham, A.: Negotiation mechanism for self-organized scheduling system with collective intelligence. Neurocomputing 132, 97–110 (2014)
Ross, S.: Introduction to Probability Models. Academic Press, Cambridge (2006)
Hillier, F.S., Lieberman, G.J.: Introduction to operations research. McGraw-Hill Higher Education, New York (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)
Gupta, D.: Queueing models for healthcare operations. In: Denton, B.T. (ed.) Handbook of Healthcare Operations Management, pp. 19–44. Springer, Heidelberg (2013). doi:10.1007/978-1-4614-5885-2_2
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)
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)
Cruz, F.R.B.: Optimizing the throughput, service rate, and buffer allocation in finite queueing networks. Electron. Notes Discrete 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)
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)
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)
Acknowledgement
We would like to acknowledge to Centro de Investigaciones y Desarrollo Científico at Universidad Distrital Francisco José de Caldas (Colombia) by supporting partially under Grant No. 2-602-468-14. Last, but not least, the authors would like to thank the comments of the anonymous referees that significantly improved our paper.
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López-Santana, E.R., Franco, C., Figueroa-Garcia, J.C. (2017). A Fuzzy Inference System to Scheduling Tasks in Queueing Systems. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_25
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DOI: https://doi.org/10.1007/978-3-319-63315-2_25
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