Motivated by recent development in predictive analytics, we study how to utilize future demand information to design optimal routing strategies when facing demand surges. We consider a multi-class multi-pool parallel server system with partial flexibility, where overflowing a customer to a non-primary server-pool can be associated with efficiency loss and other costs. Our results explicitly characterize how to incorporate future demand into routing decisions and quantify the benefit of doing so.

Motivated by the need for decision support tools for workload prediction and capacity planning in hospitals under the COVID-19 pandemic, we consider a queueing network consisting of two many-server stations, which models the flow of patients between medical/surgical and intensive care unit wards. We approximate the steady-state customer count using the stationary distribution of the associated diffusion model, which can be computed efficiently and be used as a real-time decision support tool.

Motivated by the challenges in census prediction in data-driven settings for hospital resource management, this paper introduces and studies the patient census calibration problem for hospital networks operating in a random environment. We use the expectation-maximization method to efficiently solve the calibration problem. We present simulation results that demonstrate the efficiency and accuracy of the method, and theoretical analyses that provide large-sample statistical guarantees/