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Load effect on service times

Author

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  • Delasay, Mohammad
  • Ingolfsson, Armann
  • Kolfal, Bora
  • Schultz, Kenneth

Abstract

In this paper, we develop a general framework to analyze the influence of system load on service times in queueing systems. Our framework unifies previous results and ties them to possible future studies to help empirical and analytical researchers to investigate and model the ways in which load impacts service times. We identify three load characteristics: changeover, instantaneous load, and extended load. The load characteristics induce behaviors, or mechanisms, in at least one of the system components: the server, the network, and the customer. A mechanism influences the service-time determinants: the work content, service speed, or in-process delay. We identify and define mechanisms that cause service times to change with load and use the framework to categorize them. We argue that an understanding of the relationship between load and service times can come about only by understanding the underlying mechanisms.

Suggested Citation

  • Delasay, Mohammad & Ingolfsson, Armann & Kolfal, Bora & Schultz, Kenneth, 2019. "Load effect on service times," European Journal of Operational Research, Elsevier, vol. 279(3), pages 673-686.
    • Handle: RePEc:eee:ejores:v:279:y:2019:i:3:p:673-686
    DOI: 10.1016/j.ejor.2018.12.028
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    Cited by:

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    3. Galit B. Yom-Tov & Anat Rafaeli, 2022. "Integrating emotional load into service operations," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 565-567, April.
    4. Xu, Shuling & Hall, Nicholas G., 2021. "Fatigue, personnel scheduling and operations: Review and research opportunities," European Journal of Operational Research, Elsevier, vol. 295(3), pages 807-822.
    5. Benjamin Legros, 2022. "The principal-agent problem for service rate event-dependency," Post-Print hal-03605421, HAL.
    6. Ingolfsson, Armann & Almehdawe, Eman & Pedram, Ali & Tran, Monica, 2020. "Comparison of fluid approximations for service systems with state-dependent service rates and return probabilities," European Journal of Operational Research, Elsevier, vol. 283(2), pages 562-575.
    7. Sezer Ülkü & Chris Hydock & Shiliang Cui, 2022. "Social Queues (Cues): Impact of Others’ Waiting in Line on One’s Service Time," Management Science, INFORMS, vol. 68(11), pages 7958-7976, November.
    8. Onay, Anita & Stampfer, Christina & Missbauer, Hubert, 2023. "A behavioral perspective on workload control concepts: The influence of order release on operators’ reaction behavior," International Journal of Production Economics, Elsevier, vol. 264(C).
    9. Ahmad M. Ashkanani & Benjamin B. Dunford & Kevin J. Mumford, 2022. "Impact of Motivation and Workload on Service Time Components: An Empirical Analysis of Call Center Operations," Management Science, INFORMS, vol. 68(9), pages 6697-6715, September.
    10. Heuser, Patricia & Tauer, Björn, 2023. "Single-machine scheduling with product category-based learning and forgetting effects," Omega, Elsevier, vol. 115(C).
    11. Smirnov, Dmitry & Huchzermeier, Arnd, 2020. "Analytics for labor planning in systems with load-dependent service times," European Journal of Operational Research, Elsevier, vol. 287(2), pages 668-681.
    12. Cildoz, Marta & Ibarra, Amaia & Mallor, Fermin, 2020. "Coping with stress in emergency department physicians through improved patient-flow management," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    13. D’Auria, Bernardo & Adan, Ivo J.B.F. & Bekker, René & Kulkarni, Vidyadhar, 2022. "An M/M/c queue with queueing-time dependent service rates," European Journal of Operational Research, Elsevier, vol. 299(2), pages 566-579.
    14. René Bekker, 2022. "Validating state-dependent queues in health care," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 505-507, April.
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