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Capacity planning of a perinatal network with generalised loss network model with overflow

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  • Asaduzzaman, Md
  • Chaussalet, Thierry J.

Abstract

Recent literature shows that the arrival and discharge processes in hospital intensive care units do not satisfy the Markovian property, that is, interarrival times and length of stay tend to have a long tail. In this paper we develop a generalised loss network framework for capacity planning of a perinatal network in the UK. Decomposing the network by hospitals, each unit is analysed with a GI/G/c/0 overflow loss network model. A two-moment approximation is performed to obtain the steady state solution of the GI/G/c/0 loss systems, and expressions for rejection probability and overflow probability have been derived. Using the model framework, the number of required cots can be estimated based on the rejection probability at each level of care of the neonatal units in a network. The generalisation ensures that the model can be applied to any perinatal network for renewal arrival and discharge processes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:1:p:178-185
    DOI: 10.1016/j.ejor.2013.06.037
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    References listed on IDEAS

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    1. Md Asaduzzaman & Thierry J. Chaussalet, 2011. "An overflow loss network model for capacity planning of a perinatal network," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 403-417, April.
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    7. Dae W. Choi & Nam K. Kim & Kyung C. Chae, 2005. "A Two-Moment Approximation for the GI / G / c Queue with Finite Capacity," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 75-81, February.
    8. Litvak, Nelly & van Rijsbergen, Marleen & Boucherie, Richard J. & van Houdenhoven, Mark, 2008. "Managing the overflow of intensive care patients," European Journal of Operational Research, Elsevier, vol. 185(3), pages 998-1010, March.
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    Cited by:

    1. Samantha L. Zimmerman & Alexander R. Rutherford & Alexa Waall & Monica Norena & Peter Dodek, 2023. "A queuing model for ventilator capacity management during the COVID-19 pandemic," Health Care Management Science, Springer, vol. 26(2), pages 200-216, June.
    2. 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.
    3. De Vuyst, Stijn & Bruneel, Herwig & Fiems, Dieter, 2014. "Computationally efficient evaluation of appointment schedules in health care," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1142-1154.
    4. 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.
    5. Nico Dijk & Barteld Schilstra, 2022. "On two product form modifications for finite overflow systems," Annals of Operations Research, Springer, vol. 310(2), pages 519-549, March.
    6. van Dijk, N.M. & van der Sluis, E. & Bulder, L.N. & Cui, Y., 2024. "Flexible serial capacity allocation with intensive care application," International Journal of Production Economics, Elsevier, vol. 272(C).

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