A robust optimization model for tactical capacity planning in an outpatient setting.

Area of Science:

• Operations Research
• Healthcare Management
• Applied Mathematics

Background:

• Tactical capacity planning is crucial for healthcare resource allocation, particularly physician time.
• Uncertainty in appointment demand challenges precise matching of physician availability with patient requests.
• Scarce resources like physician time necessitate robust planning strategies.

Purpose of the Study:

• To develop a robust optimization model for tactical capacity planning in outpatient settings.
• To ensure feasible allocation of physician resources against demand uncertainty.
• To evaluate the model's performance and identify critical workload periods.

Main Methods:

• Utilizing cardinality-constrained robust optimization for capacity planning.
• Modeling outpatient clinics with first-visit and re-visit patients, including access time targets.
• Experimentally evaluating the robust model under varying conservatism levels.

Main Results:

• Guaranteed 100% feasibility of the tactical capacity plan across all demand scenarios.
• Demonstrated cost savings by avoiding full conservatism while meeting demand.
• Identified critical time periods of worst-case physician peak load.

Conclusions:

• The robust optimization model provides a feasible and cost-effective solution for healthcare capacity planning under uncertainty.
• The model aids decision-makers in understanding and mitigating physician workload peaks.
• Translating data into uncertainty sets significantly impacts the conservatism of the planning solution.