NURSE SCHEDULING AND RESCHEDULING UNDER UNCERTAINTY

Abstract

Nurse planning decisions play a critical role on hospital budgeting, quality of nursing services and job satisfaction of nurses. Nurse planning in a hospital is composed of four main phases: nurse budgeting, nurse scheduling (rostering), nurse rescheduling (staffing) and nurse assignment. In this study, the integrated problem of nurse scheduling and rescheduling under demand uncertainty is considered. The problem is formulated and solved as a two-stage stochastic integer program. The value of the stochastic solution is estimated using realistic problem instances over a monthly planning horizon generated based on the data provided by a private healthcare provider in Ankara, Turkey. The computational results show that the value of the stochastic solution can be more than 9% in some problem instances, and hence healthcare providers can benefit from using models that capture uncertainty rather than deterministic models.