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dc.date.accessioned2015-12-22T10:43:42Z
dc.date.available2015-12-22T10:43:42Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10852/48337
dc.description.abstractThis thesis presents new optimisation methods for daily planning in hospitals. Implemented in software, these methods can contribute to shorter waiting times for urgently ill patients, such as cancer patients, as well a better utilisation of expensive hospital resources. One of the main challenges a hospital faces is to give proper treatment to each patient, at the right time, using the hospital's limited resources. To achieve this, it is critical that the treatment of all patients is planned as well as possible. The optimal plan is one that optimises the patients' waiting time, the number of cancellations, staff overtime, and so on. Today, this planning is done using simple rules for scheduling each patient, based on the planner's experience. While this works, from day to day, the resulting plans are often not optimal. Finding the optimal plan is very difficult, because of the inherent complexity of the underlying, mathematical, optimisation problem. Better planning therefore requires that the planner has access to powerful optimisation tools. The optimisation methods that are presented in this thesis contribute towards the development of such tools. The focus of this work has been on modelling and solving complex, real life, planning problems, without introducing unnecessary simplifications. Also, a point has been made to develop a general mathematical model that can capture a wide range of planning situations, including for example the scheduling of patients for surgery, diagnostic services, therapeutic services, or cancer treatment. Based on this model, different exact and approximate optimisation methods have been developed. The methods have been validated based on realistic data from different planning situations in different Norwegian Hospitals.
dc.language.isoenen_US
dc.relation.haspartPAPER I: Local search for the surgery admission planning problem. A. Riise and E.K. Burke. Journal of Heuristics, 17(4) (2011), pp. 389-414. DOI: 10.1007/s10732-010-9139-x The paper is available in DUO: http://urn.nb.no/URN:NBN:no-52263
dc.relation.haspartPAPER II: On parallel local search for permutations. A. Riise and E.K. Burke. Journal of the Operational Research Society 65(5) (2014) http://dx.doi.org/10.1057/jors.2014.29
dc.relation.haspartPAPER III: Modelling and solving generalised operational surgery scheduling problems. A. Riise, C. Mannino, and E.K. Burke. Computers and Operations Research (2015). The paper is not available in DUO due to publisher restrictions. The published version is available at: http://dx.doi.org/10.1016/j.cor.2015.07.003
dc.relation.haspartPAPER IV: Recursive logic-based Benders' decomposition for multi-mode outpatient scheduling. A. Riise, C. Mannino, and L. Lamorgese. Submitted to European Journal of Operational Research, August 2015. To be published. The paper is not available in DUO awaiting publishing.
dc.relation.haspartPAPER V: Scheduling and sequencing. E.K. Burke, T. Curtois, T.E. Nordlander, and A. Riise. In: Handbook of Healthcare Delivery Systems, CRC Press (2010). The paper is not available in DUO due to publisher restrictions. The published version is available at: http://www.crcnetbase.com/isbn/9781439803622
dc.relation.urihttp://urn.nb.no/URN:NBN:no-52263
dc.titleIntegrated planning and scheduling in operational patient managementen_US
dc.typeDoctoral thesisen_US
dc.creator.authorRiise, Atle
dc.identifier.urnURN:NBN:no-52262
dc.type.documentDoktoravhandlingen_US
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/48337/1/1697_Riise_materie-DUO.pdf


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