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A Framework for Optimal Recruitment of Temporary and Permanent Healthcare Workers in Highly Uncertain Environments

Malaki, S. ORCID: 0000-0002-2684-1364, Izady, N. & de Menezes, L. M. ORCID: 0000-0001-9155-5850 (2022). A Framework for Optimal Recruitment of Temporary and Permanent Healthcare Workers in Highly Uncertain Environments. European Journal of Operational Research,

Abstract

There has been a significant increase in the demand for temporary skilled workers in the health sector. They provide volume flexibility, but are generally more expensive than their permanent counterparts. In this paper, we propose a two-stage stochastic optimization framework to inform recruitment decision making for a period of highly uncertain demand in a setting where all patients must be served. The first stage identifies the number of permanent positions to advertise, and the second stage determines the number of temporary workers to recruit. Our framework accounts for the uncertainty in the permanent recruitment process, stochasticity of the service delivery, and asymmetry in demand information at the times of permanent and temporary recruitment. Under a general setting of the problem, we characterize the optimal first- and second-stage decisions analytically, propose fast numerical methods for finding their values, and prove some of their monotonicity properties. A case study based on data from a geriatric ward illustrates the application of our framework, and numerical experiments provide further managerial insights.

Publication Type: Article
Additional Information: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: OR in health services, Blended workforce recruitment, Queueing, Stochastic optimization, Demand and supply uncertainty
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
R Medicine > R Medicine (General)
Departments: Bayes Business School > Management
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