Simulation Optimization of Practical Concurrent Service Systems

  • Tad GonsalvesEmail author
  • Kiyoshi Itoh
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 52)


Concurrent service systems are modeled using the Generalized Stochastic Petri Nets (GSPN) to account for the multiple asynchronous activities within the system. The simulated operation of the GSPN modeled system is then optimized using the Particle Swarm Optimization (PSO) meta-heuristic algorithm. The objective function consists of the service costs and the waiting costs. Service cost is the cost of hiring service-providing professionals, while waiting cost is the estimate of the loss to business as some customers might not be willing to wait for the service and may decide to go to the competing organizations. The optimization is subject to the management and to the customer satisfaction constraints. The tailor-made PSO is found to converge rapidly yielding optimum results for the operation of a practical concurrent service system.


Concurrent service systems Optimization Meta-heuristics Swarm Intelligence Particle Swarm Optimization 



This work has been supported by the Open Research Center Project funds from “MEXT” of the Japanese Government (2007–2011).


  1. 1.
    Anderson, D.R., Sweeney, D.J., & Williams, T.A. (2003). An introduction to management science: Quantitative approaches to decision making (10th ed.). Ohio: Thomson South-Western.Google Scholar
  2. 2.
    Dube-Rioux, L., Schmitt, B.H., & Leclerc, F. (1988). Consumer’s reactions to waiting: when delays affect the perception of service quality. In T. Srull (Ed.), Advances in consumer research, Association for Consumer Research, 16, 59–63.Google Scholar
  3. 3.
    Fishman, G.S. (1978). Principles of discrete event simulation. New York: Wiley.zbMATHGoogle Scholar
  4. 4.
    Folkes, V.S. (1984). Consumer reactions to product failure: an attributional approach. Journal Consumer Research, 10(4), 298–409.Google Scholar
  5. 5.
    Folkes, V.S., Koletsky, S., & Graham, J.L. (1987). A field study of causal inferences and consumer reaction: the view from the airport. Journal of Consumer Behavior, 13, 534–539.Google Scholar
  6. 6.
    Hillier, F.S. (1963). Economic models for industrial waiting line problems. Management Science, 10(1), 119–130.CrossRefGoogle Scholar
  7. 7.
    Holliday, M.A., & Vernon, M.K. (1985). A generalized timed Petri net model for performance evaluation. In Proceedings of the international workshop timed Petri nets, Torino (pp. 181–190).Google Scholar
  8. 8.
    Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization. Proceedings of the IEEE international conference neural network (pp. 1942–1948). PiscatawayGoogle Scholar
  9. 9.
    Kennedy, J., Eberhart R.C., & Shi, Y. (2001). Swarm Intelligence. San Francisco, CA: Morgan Kaufmann.Google Scholar
  10. 10.
    Maister, D.H. (1985). The psychology of waiting lines. In J. Czepiel, M.R. Solomon, & C.F. Surprenant (Eds.), The service encounter (pp. 113–123). Lexington, MA: Lexington Books.Google Scholar
  11. 11.
    Molloy, M.K. (1982). Performance analysis using stochastic Petri nets. IEEE Transactions on Computers, 31(9), 913–917.CrossRefGoogle Scholar
  12. 12.
    Murata, T. (1989). Petri nets – properties, analysis, and applications. Proceedings of the IEEE, 77(4), 541–580.CrossRefGoogle Scholar
  13. 13.
    Ozcan, Y.A. (2005). Quantitative methods in health care management: techniques and applications. San Francisco, CA: Jossey-Bass/Wiley.Google Scholar
  14. 14.
    Peterson, J.L. (1981). Petri net theory and the modeling of systems. New York: Prentice-Hall.Google Scholar
  15. 15.
    Ramamoorthy C.V., & Ho, G.S. (1980). Performance evaluation of asynchronous concurrent systems using Petri nets, IEEE Transactions on Software Engineering, 6(5), 440–449.MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Razouk, R.R. (1984). The derivation of performance expressions for communication protocols from timed Petri nets. Computer Communications Review, 14(2), 210–217.CrossRefGoogle Scholar
  17. 17.
    Reeves, C. (2003). Genetic algorithms. In F. Glover, & G.A. Kochenberger (Eds.), Handbook of metaheuristics. Boston: Kluwer.Google Scholar
  18. 18.
    Scotland, R. (1991). Customer service: a waiting game. Marketing, 11, 1–3.Google Scholar
  19. 19.
    Taylor, S. (1994). Waiting for service: the relationship between delays and evaluation of service. Journal of Marketing, 58, 56–69.CrossRefGoogle Scholar
  20. 20.
    Taylor, S. (1995). The effects of filled waiting time and service provider control over the delay on evaluations of service. Journal of Academic Marketing Science, 23(1), 38–48.CrossRefGoogle Scholar
  21. 21.
    Terano, T. Asai, K., & Sugeno, M. (1992). Fuzzy systems theory and its applications. Boston: Academic.zbMATHGoogle Scholar
  22. 22.
    Turksen, I.B. (1991). Measurement of membership functions and their acquisition, Fuzzy Sets and Systems, 40, 5–38.MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–359.MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Zimmermann, H.-J. (1991). Fuzzy set theory and its applications (2nd ed.). Boston: Kluwer.zbMATHGoogle Scholar
  25. 25.
    Zuberek, W.M. (1988). D-timed Petri nets and modelling of timeouts and protocols. Transactions of the Society for Computer Simulation, 4(4), 331–357.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Information and Communication SciencesSophia UniversityChiyoda-kuJapan

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