Optimization of surgery sequencing and scheduling decisions under uncertainty

Area of Science:

• Healthcare Operations Research
• Applied Optimization
• Hospital Management

Background:

• Operating rooms (ORs) are critical hospital cost centers and revenue generators.
• Uncertainty in surgery durations poses significant challenges for OR scheduling.
• Delayed surgeries lead to cascading schedule disruptions and increased overtime costs.

Purpose of the Study:

• To develop a stochastic optimization model for OR scheduling.
• To identify practical heuristics for hedging against surgery duration uncertainty.
• To simultaneously optimize surgery sequencing and start times.

Main Methods:

• Developed a stochastic optimization model for OR scheduling.
• Incorporated practical heuristics to manage uncertainty in surgery durations.
• Focused on sequencing surgeries and scheduling start times concurrently.

Main Results:

• A simple sequencing rule based on surgery duration variance was identified.
• This rule substantially reduced surgeon/OR team waiting and OR idling costs.
• Significant reductions in overtime costs were also achieved.

Conclusions:

• Stochastic optimization models and heuristics can improve OR scheduling efficiency.
• Sequencing surgeries by duration variance is an effective cost-saving strategy.
• Real-world case studies validate the model's effectiveness in reducing operational costs.