Journal article

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

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

European Journal of Operational Research | Elsevier | Published : 2018

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 ..

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Grants

Awarded by Australian Research Council (ARC)


Awarded by ARC through Laureate Fellowship


Funding Acknowledgements

We would like to thank Olivia Smith for her ideas to fine tune the solver parameters. Furthermore, this research project is financially supported by an Australian Government Research Training Program Scholarship and Australian Research Council (ARC) linkage grant LP140100152. Peter Taylor would like to acknowledge the support of the ARC through Laureate Fellowship FL130100039 and the ARC Centre of Excellence for the Mathematical and Statistical Frontiers (ACEMS).