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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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Equity-promoting integer programming approaches for medical resident rotation scheduling.

Shutian Li1, Karmel S Shehadeh2, Frank E Curtis1

  • 1Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA.

Health Care Management Science
|November 22, 2025
PubMed
Summary
This summary is machine-generated.

We developed new integer programming methods to solve the resident-to-rotation assignment problem, optimizing schedules and improving fairness in vacation request fulfillment for medical residents.

Keywords:
FairnessInteger programmingOperations managementOperations researchOptimizationResident scheduling

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Area of Science:

  • Operations Research
  • Medical Education
  • Computer Science

Background:

  • The resident-to-rotation assignment problem (RRAP) is complex, involving numerous constraints and preferences.
  • Manual scheduling methods are time-consuming and can lead to inequities in resident satisfaction, particularly regarding vacation requests.

Purpose of the Study:

  • To develop novel integer programming (IP) approaches for the RRAP.
  • To create an equity-promoting model that balances maximizing satisfied vacation requests with minimizing disparities.
  • To design a computationally efficient algorithm for practical implementation.

Main Methods:

  • Formulated an IP model mirroring current manual scheduling practices.
  • Developed an equity-promoting IP counterpart to address vacation request disparities.
  • Proposed a Pareto Search Algorithm to efficiently find optimal solutions.
  • Created a user-friendly tool for automated schedule generation.

Main Results:

  • The proposed IP approaches are computationally efficient and implementable.
  • The equity-promoting model successfully maximizes satisfied vacation requests while reducing disparities.
  • The Pareto Search Algorithm effectively identifies Pareto optimal solutions within practical timeframes.
  • Extensive experiments demonstrate the practical benefits and fairness enhancements.

Conclusions:

  • The developed IP models and algorithms offer a significant improvement over manual RRAP methods.
  • These approaches enhance fairness and efficiency in resident rotation scheduling.
  • The user-friendly tool facilitates practical adoption in academic health systems.