Demand prediction and dynamic workforce allocation to improve airport screening operations

Workforce allocation and configuration decisions at airport security checkpoints (e.g., number of lanes open) are usually based on passenger volume forecasts. The accuracy of such forecasts is critical for the smooth functioning of security checkpoints where unexpected surges in passenger volumes are handled proactively. In this article, we present a forecasting model that combines flight schedules and other business fundamentals with historically observed throughput patterns to predict passenger volumes in a multi-terminal multi-security screening checkpoint airport. We then present an optimization model and a solution strategy for dynamically selecting a configuration of open screening lanes to minimize passenger queues and wait times that at the same time determine workforce allocations. We present a real-world case study in a US airport to demonstrate the efficacy of the proposed models.