HOSPITAL CAPACITY PLANNING USING AN OPTIMIZATION MODEL: AN APPROACH APPLIED TO A PUBLIC HOSPITAL X

Hospital capacity planning involves complex trade-offs between uncertain demand, limited resources, and financial constraints. This study proposes a Mixed-Integer Programming (MIP) model for integrated capacity planning at the Public Hospital X, within the Brazilian Unified Health System (SUS). The model simultaneously determines demand allocation, service capacity, and resource sizing to maximize net operating income under operational constraints.

The formulation incorporates patient prioritization (Manchester Triage System), spatial demand allocation via an exponential gravity function, and detailed cost structures, including fiscal benefits. Results indicate latent productive capacity, with potential SUS revenue 104% higher than the historical budget. The solution identifies structural bottlenecks in diagnostic and inpatient services and highlights the need for expanding beds and hospital rooms.

The model provides a robust decision-support framework, integrating clinical, spatial, and economic factors, with applicability to public hospitals under resource constraints.