A mixed integer programming approach for allocating operating room capacity
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We have developed a methodology for allocating operating room capacity to specialties. Our methodology consists of a finite-horizon mixed integer programming (MIP) model which determines a weekly operating room allocation template that minimizes inpatients' cost measured as their length of stay. A number of patient type priority (eg emergency over non-emergency patient) and clinical constraints (eg maximum number of hours allocated to each specialty, surgeon, and staff availability) are included in the formulation. The optimal solution from the analytical model is inputted into a simulation model that captures some of the randomness of the processes (eg surgery time, demand, arrival time, and no-show rate of the outpatients) and non-linearities (eg the MIP assumes proportional allocation of demand satisfaction (output) with room allocation (input)). The simulation model outputs the average length of stay for each specialty and the room utilization. On a case example of a Los Angeles County Hospital, we show how the hospital length of stay pertaining to surgery can be reduced.
Keywordsmixed integer programming surgery operating room capacity block time scheduling simulation
- Blake JT, Dexter F and Donald J (2002). Operating room managers' use of integer programming for assigning block time to surgical groups: a case study. Anesth Analg 94: 143–148.Google Scholar
- Clinical Scholars Program (2006). Interim Report July 2005–February 2006, UCLA—The Robert Wood Johnson Foundation. http://www.hsrcenter.ucla.edu/csp/sitevisit2006/Handouts/REVISED_UCLARJW_05-06_Internimrpt.doc, accessed 29 April 2007.
- Dexter F, Macario A, Traub RD, Hopwood M and Lubarsky DA (1999b). An operating room scheduling strategy to maximize the use of operating room block time: computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time. Anesth Analg 89: 7–20.Google Scholar
- Kourie DG (1975). A length of stay index to monitor efficiency of service to general surgery in-patients. Opl Res Quart 26 (Part 1): 63–69.Google Scholar
- Litvak E and Long MC (2000). Cost and quality under managed care: Irreconcilable differences. Am J Managed Care 6: 305–312.Google Scholar
- Pritsker AAB and O'Reilly JJ (1999). Simulation with Visual SLAM and AweSim, (2nd edn). John Wiley & Sons, New York, and Systems Publishing Corporation: West Lafayette, Indiana.Google Scholar