The full text of this article


An incentive-based method for hospital capacity management in a pandemic: the assignment approach
by Lihui Bai; Jiang Zhang
International Journal of Mathematics in Operational Research (IJMOR), Vol. 6, No. 4, 2014


Abstract: We consider a hospital capacity management problem that addresses the drastic surge of patient volume during a pandemic. This paper is novel in that it allows patients to choose hospitals on their own, whereas most literature assumes that patients will go to their assigned hospitals. We propose an incentive-based approach to help direct patients to alternative hospitals so that capacity shortages across all hospitals are balanced. Consequently, the hospital resources for the community as a whole are utilised most efficiently. The proposed approach is based on two assignment models. One is a (decentralised) equilibrium model for describing patients' choice of hospital. The other is a (centralised) non-linear programming model for the health authority to maximise the resource utilisation of all the hospitals in the region. We show that when responding to incentive programmes at properly chosen hospitals, the patients' choice of hospitals can match the one desired by the central health authority, i.e., the one that utilises the overall resources most efficiently. Numerical examples are used to illustrate our approach.

Online publication date: Fri, 04-Jul-2014


is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematics in Operational Research (IJMOR):
Login with your Inderscience username and password:


    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email