A master surgical scheduling approach for cyclic scheduling in operating room departments

Jeroen M. van Oostrum*, Mark van Houdenhoven, Johann L. Hurink, Erwin W. Hans, Gerhard Wullink, Geert Kazemier

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

179 Citations (Scopus)
24 Downloads (Pure)

Abstract

This paper addresses the problem of operating room (OR) scheduling at the tactical level of hospital planning and control. Hospitals repetitively construct operating room schedules, which is a time-consuming, tedious, and complex task. The stochasticity of the durations of surgical procedures complicates the construction of operating room schedules. In addition, unbalanced scheduling of the operating room department often causes demand fluctuation in other departments such as surgical wards and intensive care units. We propose cyclic operating room schedules, so-called master surgical schedules (MSSs) to deal with this problem. In an MSS, frequently performed elective surgical procedure types are planned in a cyclic manner. To deal with the uncertain duration of procedures we use planned slack. The problem of constructing MSSs is modeled as a mathematical program containing probabilistic constraints. Since the resulting mathematical program is computationally intractable we propose a column generation approach that maximizes the operation room utilization and levels the requirements for subsequent hospital beds such as wards and intensive care units in two subsequent phases. We tested the solution approach with data from the Erasmus Medical Center. Computational experiments show that the proposed solution approach works well for both the OR utilization and the leveling of requirements of subsequent hospital beds.
Original languageEnglish
Pages (from-to)355-374
Number of pages20
JournalOR Spectrum = OR Spektrum
Volume30
Issue number2
DOIs
Publication statusPublished - Apr 2008

Keywords

  • MSC-90B35
  • Scheduling
  • Master surgical schedules
  • Healthcare planning
  • Mathematical modeling

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