Skip to main content
Log in

A service system with on-demand agent invitations

  • Published:
Queueing Systems Aims and scope Submit manuscript

Abstract

We consider a service system where agents are invited on-demand. Customers arrive exogenously as a Poisson process and join a customer queue upon arrival if no agent is available. Agents decide to accept or decline invitations after some exponentially distributed random time, and join an agent queue upon invitation acceptance if no customer is waiting. A customer and an agent are matched in the order of customer arrival and agent invitation acceptance under the non-idling condition, and will leave the system simultaneously once matched (service times are irrelevant here). We consider a feedback-based adaptive agent invitation scheme, which controls the number of pending agent invitations, depending on the customer and/or agent queue lengths and their changes. The system process has two components— ‘the difference between agent and customer queues’ and ‘the number of pending invitations,’ and is a countable continuous-time Markov chain. For the case when the customer arrival rate is constant, we establish fluid and diffusion limits, in the asymptotic regime where the customer arrival rate goes to infinity, while the agent response rate is fixed. We prove the process stability and fluid-scale limit interchange, which in particular imply that both customer and agent waiting times in steady-state vanish in the asymptotic limit. To do this we develop a novel (multi-scale) Lyapunov drift argument; it is required because the process has non-trivial behavior on the state space boundary. When the customer arrival rate is time-varying, we present a fluid limit for the processes in the same asymptotic regime. Simulation experiments are conducted to show good performance of the invitation scheme and accuracy of fluid limit approximations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Aksin, Z., Armony, M., Mehrotra, V.: The modern call center: a multi-disciplinary perspective on operations management research. Product. Oper. Manag. 16(6), 665–688 (2007)

    Article  Google Scholar 

  2. American Telemedicine Association (2014) Core operational guidelines for telehealth services involving provider-patient interactions. http://www.americantelemed.org/docs/default-source/standards/core-operational-guidelines-for-telehealth-services?sfvrsn=6

  3. Azriel, D., Feigin, P.D., Mandelbaum, A.: Erlang S: A data-based model of servers in queueing networks. Working paper (2014)

  4. Bengtson, S.: Generating better results with crowdsourcing: Leverage a network of high-quality professionals for customer service. White paper. http://www.arise.com/ (2014)

  5. Formisano, P.: Flexibility for changing business needs: Improve customer service and drive more revenue with a virtual crowdsourcing solution. White paper. http://www.arise.com/ (2014)

  6. Gamarnik, D., Goldberg, D.: Steady-state \(GI/GI/n\) queue in the Halfin–Whitt regime. Ann. Appl. Probab. 23(6), 2382–2419 (2013)

    Article  Google Scholar 

  7. Gamarnik, D., Momcilovic, P.: Steady-state analysis of a multi-server queue in the Halfin–Whitt regime. Advan. Appl. Probab. 40(2), 548–577 (2008)

    Article  Google Scholar 

  8. Gamarnik, D., Stolyar, A.L.: Multiclass multi-server queueing system in the Halfin–Whitt heavy-traffic regime: asymptotics of the stationary distribution. Queueing Syst. 71(1), 25–51 (2012)

    Article  Google Scholar 

  9. Gans, N., Koole, G., Mandelbaum, A.: Telephone call centers: tutorial, review and research prospects. Manuf. Serv. Oper. Manag. 5(2), 79–141 (2003)

    Google Scholar 

  10. Gershwin, S.B.: Lecture notes on inventory. http://ocw.mit.edu/courses/mechanical-engineering/2-854-introduction-to-manufacturing-systems-fall-2010/lecture-notes/MIT2_854F10_inv (2010)

  11. Gurvich, I., Lariviere, M., Moreno-Garcia, A.: Operations in the on-demand economy: staffing services with self-scheduling capacity. Working paper (2015)

  12. Gurvich, I., Ward, W.: On the dynamic control of matching queues. Stoch. Syst. 4(2), 479–523 (2014)

    Article  Google Scholar 

  13. Ibrahim, R.: Capacity sizing in queueing models with a random number of servers. Working paper (2015)

  14. Karatzas, I., Shreve, S.: Brownian Motion and Stochastic Calculus, 2nd edn. Springer, New York (1996)

    Google Scholar 

  15. Kashyap, B.R.K.: The double-ended queue with bulk service and limited waiting space. Oper. Res. 14(5), 822–834 (1966)

    Article  Google Scholar 

  16. Kurtz, T.G.: Strong approximation theorems for density dependent Markov chains. Stoch. Process. Appl. 6(3), 223–240 (1978)

    Article  Google Scholar 

  17. Liu, X., Gong, Q., Kulkarni, V.G.: Diffusion models for doubly-ended queues with renewal arrival processes. Forthcoming in Stochastic Systems (2014). doi:10.1214/13-SSY113

  18. McGee-Smith, S.: Why companies are choosing to deploy the LiveOps cloud-based contact center. http://www.liveops.com/sites/default/files/uploads/lo_wp_mcgee-smith_analytics (2010)

  19. Meyn, S.P., Tweedie, R.L.: Markov Chains and Stochastic Stability, 2nd edn. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  20. Pang, G., Talreja, R., Whitt, W.: Martingale proofs of many-server heavy-traffic limits for Markovian queues. Probab. Surv. 4, 193–267 (2007)

    Article  Google Scholar 

  21. Stolyar, A.L.: Control of end-to-end delay tails in a multiclass network: LWDF discipline optimality. Ann. Appl. Probab. 13(3), 1151–1206 (2003)

    Article  Google Scholar 

  22. Stolyar, A.L., Reiman, M.I., Korolev, N., Mezhibovsky, V., Ristock, H.: Pacing in knowledge worker engagement. United States Patent Application 20100266116-A1 (October 2010)

  23. Stolyar, A.L., Yudovina, E.: Tightness of invariant distributions of a large-scale flexible service system under a priority discipline. Stochastic Systems. 2(2), 381–408. arXiv:1201.2978 (2012)

  24. Stolyar, A.L.: Diffusion scale tightness of invariant distributions of a large-scale flexible service system. Advances in Applied Probability. 47(1), 251–269. arXiv:1301.5838 (2015)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guodong Pang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pang, G., Stolyar, A.L. A service system with on-demand agent invitations. Queueing Syst 82, 259–283 (2016). https://doi.org/10.1007/s11134-015-9464-8

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11134-015-9464-8

Keywords

Mathematics Subject Classification

Navigation