Integrating Regime-Switching Probabilistic Forecasts into Stochastic Optimization for Nurse Rostering in Mental Health Wards
Mental Health
Healthcare Operations
Sequential Decision Analytics
Python
Time Series Forecasting
Machine Learning
The 24th Conference of the International Federation of Operational Research Societies
Date: July 11 2026 12:00-17:00
Event: IFORS 2026
Location: University of Vienna, Austria
Context
At the 24th Conference of the International Federation of Operational Research Societies (IFOR 2026), I presented our research on “Integrating Regime-Switching Probabilistic Forecasts into Stochastic Optimization for Nurse Rostering in Mental Health Wards.” This presentation highlighted our novel approach of using Autoregressive Markov-switching regression (AR-MSR) to forecast occupancy levels in mental hospital wards. The forecasts were then incorporated into a two-stage stochastic programming model to optimize nurse rostering decisions, with the goal of enhancing patient care and operational efficiency in mental health settings.