Learning to Staff from Operational Proxies: Towards Win-Win On-Demand Staffing

On-demand staffing plans labor in advance to meet changing business needs. Ideally, it achieves a win-win outcome for businesses and workers: reducing unnecessary labor-hours while meeting business needs and keeping workloads manageable. However, historical schedules record assigned staffing rather than minimum requirements, so learning from them can reproduce overstaffing. In this paper, we propose ProxyStaff, a general supervision framework that learns staffing requirements from paired business and scheduling records. An operational proxy relates each role's labor to supported revenue and transactions, treating required roles as complementary stages. Its inverse yields minimum staffing requirements under the proxy. These labels enable different backbone models to learn future hourly staffing needs directly from planning-time information, through training from scratch or fine-tuning pretrained models. To evaluate ProxyStaff, we collect two real-world datasets from a restaurant chain with 13 stores and a milk-tea chain with 8 stores. We evaluate 6 backbone models for planning 1 or 7 days in advance, reflecting managers' routine lead times for publishing staffing needs. Across multiple backbones, ProxyStaff more often matches human-manager business support with fewer labor-hours than learning historical assignments or forecasting business before converting it to staffing. The results show that ProxyStaff can consistently reduce labor-hours by about 20–30% relative to human-manager schedules across both chains and planning horizons while matching or exceeding their business support under the proxy.