Staffordshire University logo
STORE - Staffordshire Online Repository

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

ASADUZZAMAN, Md and Chaussalet, Thierry (2014) Capacity planning of a perinatal network with generalised loss network model with overflow. European Journal of Operational Research, 232 (1). pp. 178-185. ISSN 0377-2217

This is the latest version of this item.

[img]
Preview
Text
asaduzzaman_et_al_ejor_final_V.pdf

Download (328kB) | Preview

Abstract or description

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.

Item Type: Article
Subjects: G200 Operational Research
Faculty: Previous Faculty of Computing, Engineering and Sciences > Engineering
Depositing User: Md ASADUZZAMAN
Date Deposited: 12 Aug 2014 15:41
Last Modified: 12 Aug 2014 15:41
URI: http://eprints.staffs.ac.uk/id/eprint/1938

Available Versions of this Item

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000