A sequential stochastic mixed integer programming model for tactical master surgery scheduling

Ashwani Kumar, Alysson M. Costa, Mark Fackrell and Peter G. Taylor

European Journal of Operational Research, 2018, vol. 270, issue 2, 734-746

Abstract: In this paper, we develop a stochastic mixed integer programming model to optimise the tactical master surgery schedule (MSS) in order to achieve a better patient flow under downstream capacity constraints. We optimise the process over several scheduling periods and we use various sequences of randomly generated patients’ length of stay scenario realisations to model the uncertainty in the process. This model has the particularity that the scenarios are chronologically sequential, not parallel. We use a very simple approach to enhance the non-anticipative feature of the model, and we empirically demonstrate that our approach is useful in achieving the desired objective. We use simulation to show that the most frequently optimal schedule is the best schedule for implementation. Furthermore, we analyse the effect of varying the penalty factor, an input parameter that decides the trade-off between the number of cancellations and occupancy level, on the patient flow process. Finally, we develop a robust MSS to maximise the utilisation level while keeping the number of cancellations within acceptable limits.

Keywords: OR in health services; Patient flow; Stochastic scheduling; Elective surgery; Tactical master surgery schedule (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:270:y:2018:i:2:p:734-746

DOI: 10.1016/j.ejor.2018.04.007

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