Service Systems

  • S. BhatnagarEmail author
  • H. Prasad
  • L. Prashanth
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 434)


Service-based economies and business models have gained significant importance in recent times. The clients and service providers exchange value through service interactions and reach service outcomes. Service requests of a client can vary greatly in the skills required to fulfill the request, expected turn-around time, and the context of the client’s business needs. As a result, automation of service operations has been limited and service delivery is largely a labor-intensive business. Hence, it is crucial to optimize labor costs while maintaining the desired quality of service. In this chapter, we describe a study where the stochastic optimization methods have been applied to minimize labor costs in a service system. This chapter is based on [2,3,6].


Service System Skill Level Service Level Agreement Descent Direction Order Method 
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  1. 1.
    Banerjee, D., Desai, N., Dasgupta, G.: Simulation-based evaluation of dispatching policies in service systems. In: Winter Simulation Conference (2011)Google Scholar
  2. 2.
    Prashanth, L.A., Prasad, H.L., Desai, N., Bhatnagar, S., Dasgupta, G.: Stochastic Optimization for Adaptive Labor Staffing in Service Systems. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) Service Oriented Computing. LNCS, vol. 7084, pp. 487–494. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Prashanth, L.A., Prasad, H., Desai, N., Bhatnagar, S., Dasgupta, G.: Simultaneous perturbation methods for adaptive labor staffing in service systems. Tech. rep., Stochastic Systems Lab, IISc (2012),
  4. 4.
    Laguna, M.: Optimization of complex systems with optquest. Opt. Quest for Crystal Ball User Manual, Decisioneering (1998)Google Scholar
  5. 5.
    Marbach, P., Tsitsiklis, J.: Simulation-based optimization of markov reward processes. IEEE Transactions on Automatic Control 46(2), 191–209 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Prasad, H., Prashanth, L.A., Desai, N., Bhatnagar, S.: Adaptive smoothed functional based algorithms for labor cost optimization in service systems. Tech. rep., Stochastic Systems Lab, IISc (2012),

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

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