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A loss network model with overflow for capacity planning of a neonatal unit

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  • Md Asaduzzaman
  • Thierry Chaussalet
  • Nicola Robertson

Abstract

The main aim of this paper is to derive a solution to the capacity problem faced by many perinatal networks in the United Kingdom. We propose a queueing model to determine the number of cots at all care units for any desired overflow and rejection probability in a neonatal unit. The model formulation is developed, being motivated by overflow models in telecommunication systems. Exact expressions for the overflow and rejection probabilities are derived. The model is then applied to a neonatal unit of a perinatal network in the UK. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Md Asaduzzaman & Thierry Chaussalet & Nicola Robertson, 2010. "A loss network model with overflow for capacity planning of a neonatal unit," Annals of Operations Research, Springer, vol. 178(1), pages 67-76, July.
    • Handle: RePEc:spr:annopr:v:178:y:2010:i:1:p:67-76:10.1007/s10479-009-0548-x
    DOI: 10.1007/s10479-009-0548-x
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    References listed on IDEAS

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    Cited by:

    1. Hideaki Takagi & Yuta Kanai & Kazuo Misue, 2017. "Queueing network model for obstetric patient flow in a hospital," Health Care Management Science, Springer, vol. 20(3), pages 433-451, September.
    2. Josephine Varney & Nigel Bean & Mark Mackay, 2019. "The self-regulating nature of occupancy in ICUs: stochastic homoeostasis," Health Care Management Science, Springer, vol. 22(4), pages 615-634, December.
    3. Elvan Gökalp & Nalan Gülpınar & Xuan Vinh Doan, 2020. "Capacity Planning for Networks of Stem‐Cell Donation Centers under Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 29(2), pages 281-297, February.
    4. Asaduzzaman, Md & Chaussalet, Thierry J., 2014. "Capacity planning of a perinatal network with generalised loss network model with overflow," European Journal of Operational Research, Elsevier, vol. 232(1), pages 178-185.
    5. Hui Zhang & Thomas J. Best & Anton Chivu & David O. Meltzer, 2020. "Simulation-based optimization to improve hospital patient assignment to physicians and clinical units," Health Care Management Science, Springer, vol. 23(1), pages 117-141, March.
    6. Yanting Chen & Jingui Xie & Taozeng Zhu, 2023. "Overflow in systems with two servers: the negative consequences," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 838-863, September.
    7. Jie Bai & Andreas Fügener & Jan Schoenfelder & Jens O. Brunner, 2018. "Operations research in intensive care unit management: a literature review," Health Care Management Science, Springer, vol. 21(1), pages 1-24, March.
    8. Song-Hee Kim & Ward Whitt, 2014. "Are Call Center and Hospital Arrivals Well Modeled by Nonhomogeneous Poisson Processes?," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 464-480, July.
    9. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    10. Adrien Wartelle & Farah Mourad-Chehade & Farouk Yalaoui & David Laplanche & Stéphane Sanchez, 2024. "Changing the perspective of system crowding evaluation using a new congestion measure: application to the Emergency Department," Operational Research, Springer, vol. 24(4), pages 1-35, December.
    11. Masoumeh Vali & Khodakaram Salimifard & Amir H. Gandomi & Thierry J. Chaussalet, 2022. "Care process optimization in a cardiovascular hospital: an integration of simulation–optimization and data mining," Annals of Operations Research, Springer, vol. 318(1), pages 685-712, November.
    12. Peng, Xu & Tao, Xiaoma, 2018. "Cooperative game of electricity retailers in China's spot electricity market," Energy, Elsevier, vol. 145(C), pages 152-170.

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