A German hospital automated physician scheduling with MIP, slicing hours of planning

A German hospital department's physician scheduling was manual and slow — plans built by hand, quality hard to judge. Automating it looked like a one-off software project: every hospital's rules differ, so off-the-shelf systems never fit.

The team's move was to separate the data from the model. Relational data modeling structured the complex scheduling information (physicians, shifts, qualifications, fairness preferences) so that a single mixed-integer programming formulation became practical. The optimizer builds on previous schedules and handles shift compatibility beyond the planning horizon.

The result is a decision support tool that produces rosters much faster with less work, visualizes plan quality for easy evaluation, and manages fairness explicitly across the planning horizon. The model is designed to be swiftly customized and implemented in other hospital departments.

Model the schedule's data structure, then optimize it

Relational modeling turns a 'different everywhere' problem into standard inputs, so one MIP works across departments instead of a bespoke program per hospital.

Starting from the previous schedule and honoring post-horizon shifts keeps changes incremental, which is what physicians actually accept.

Making fairness an explicit objective means the plan can be defended to staff, a requirement hand-built rosters never met.

When automation keeps failing because every instance is different, fix the data layer first: relational modeling can make one optimizer reusable across departments and hospitals.

The MIP-based scheduling system was published in the INFORMS Journal on Applied Analytics in 2026. The authors present it as quickly customizable for other hospital-internal departments and other hospitals, with the relational data model as the reusable core.