Abstract
The present paper studies patient-to-room assignment planning in a dynamic context. To this end, an extension of the patient assignment (PA) problem formulation is proposed, for which two online ILP-models are developed. The first model targets the optimal assignment for newly arrived patients, whereas the second also considers future, but planned, arrivals. Both models are compared on an existing set of benchmark instances from the PA planning problem, which serves as the basic problem setting. These instances are then extended with additional parameters to study the effect of uncertainty on the patients’ length of stay, as well as the effect of the percentage of emergency patients. The results show that the second model provides better results under all conditions, while still being computationally tractable. Moreover, the results show that pro-actively transferring patients from one room to another is not necessarily beneficial.
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Notes
Also denoted as a block scheduling strategy.
Note that the notion of a maximal clique differs from a maximum clique. A maximum clique is the largest cardinality clique that can be found in a graph.
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Acknowledgements
We would like to thank Sara Ceschia and Andrea Schaerf from the University of Udine for the many fruitful discussions on the specific subject of this study. This research is funded by a Ph.D. grant of the Agency for Innovation by Science and Technology (IWT).
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Vancroonenburg, W., De Causmaecker, P. & Vanden Berghe, G. A study of decision support models for online patient-to-room assignment planning. Ann Oper Res 239, 253–271 (2016). https://doi.org/10.1007/s10479-013-1478-1
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DOI: https://doi.org/10.1007/s10479-013-1478-1