A preference‐integrated optimization system for medical physics shift scheduling

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BACKGROUND: Clinical shift scheduling for medical physicists is challenging given multidisciplinary roles and competing clinical, research, and service demands. Manual workflows often lack the flexibility and transparency needed for this complex field. PURPOSE: To develop, deploy, and evaluate a preference-driven, optimization-based scheduling platform for an academic radiation oncology medical physics division. METHODS: Using a graphical user interface, each physicist encoded half-day shift slots as unavailable (0), available but not preferred (0.5), available (1), or preferred (2) for each two-month scheduling block over two years of clinical use. Shift requirements were defined across ten service categories with weighted effort units, and individual targets were adjusted for non-clinical effort (e.g., research, teaching, administrative service) and planned time off. Scheduling was formulated as a constrained binary integer optimization problem, with coverage, sequencing, and availability constraints, solved using a genetic algorithm to minimize a preference-based fitness function. Final schedules were exported to clinical calendars. Retrospective evaluation computed descriptive statistics per scheduling period and overall, including the distribution of assigned shifts by preference category and concordance between assignments and stated availability/preferences. Qualitative feedback from users and scheduling leads was collected to further assess operational impact. RESULTS: From January 2024 through February 2026, 4980 shifts were assigned across fifteen scheduling blocks for 23 physicists. Across 25,796 preference entries, 95% of assignments matched stated preference and availability, with non-preferred assignments accounting for only 5% of total assignments. All clinical coverage requirements were met, and no staff were scheduled for shifts marked as unavailable. Manual interventions were rare, and user feedback indicated improved transparency, satisfaction, and reduced administrative burden. CONCLUSIONS: Preference-integrated optimization reliably produced feasible, clinically robust schedules for medical physics shift scheduling. This approach is readily implementable and supports efficient, equitable scheduling in multidisciplinary settings.

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