An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process

Amin Abedini, Wei Li, Honghan Ye

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

Abstract

Operating room (OR) scheduling is important. Because of increasing demand for surgical services, hospitals must provide high quality care more efficiently with limited resources. When constructing the OR schedule, it is necessary to consider the availability of downstream resources, such as intensive care unit (ICU) and post anaesthesia care unit (PACU). The unavailability of downstream resources causes blockings between every two consecutive stages. In this paper we address the master surgical schedule (MSS) problem in order to minimize blockings between two consecutive stages. First, we present a blocking minimization (BM) model for the MSS by using integer programming, based on deterministic data. The BM model determines the OR block schedule for the next day by considering the current stage occupancy (number of patients) in order to minimize the number of blockings between intraop and postop stages. Second, we test the effectiveness of our model under variations in case times and patient arrivals, by using simulation. The simulation results show that our BM model can significantly reduce the number of blockings by 94% improvement over the base model. Scheduling patient flow across the 3-stage periop process can be applied to work flow scheduling for the s-stage flow shop shop production in manufacutirng, and also Smoothing patient flow in periop process can be applied to no-wait flow shop production.

Original languageEnglish
Pages (from-to)60-70
Number of pages11
JournalProcedia Manufacturing
Volume10
DOIs
StatePublished - 2017
Event45th SME North American Manufacturing Research Conference, NAMRC 2017 - Los Angeles, United States
Duration: Jun 4 2017Jun 8 2017

Bibliographical note

Publisher Copyright:
© 2017

Keywords

  • operating room
  • optimization
  • scheduling
  • simulation
  • statistical process control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

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