A preference-integrated optimization system for medical physics shift scheduling

Area of Science:

• Medical physics operations
• Healthcare administration
• Operations research

Background:

• Clinical shift scheduling for medical physicists is complex due to diverse roles and demands.
• Manual scheduling methods often lack flexibility and transparency.

Purpose of the Study:

• To develop and evaluate a preference-driven, optimization-based scheduling platform for medical physics in radiation oncology.
• To enhance the efficiency and equity of academic medical physics scheduling.

Main Methods:

• Physicists input preferences (unavailable, available, preferred) for shift slots.
• A constrained binary integer optimization problem was formulated and solved using a genetic algorithm.
• Schedules were evaluated based on preference matching, coverage, and user feedback.

Main Results:

• 95% of 4980 assigned shifts matched stated preferences and availability over two years.
• All clinical coverage requirements were met, with no unavailable shifts assigned.
• User feedback indicated improved transparency, satisfaction, and reduced administrative burden.

Conclusions:

• Preference-integrated optimization reliably generates feasible and clinically robust schedules.
• This approach is implementable and supports efficient, equitable scheduling in multidisciplinary settings.