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• W650628586 abstract "Since 2009, as a result of the global financial and economic crisis, the health expenditure in Organisation for Economic Co-operation and Development (OECD) countries stopped a long term rising trend and has been stagnating or even falling in many countries. The crisis forced many governments to promote challenging cuts in public expenditure. For instance, Portugal agreed with the European Union, within an economic and financial adjustment programme, to cut 15% on health costs between 2011 and 2013. In this context, many countries promoted reforms in the health sector to increase productivity and efficiency. In addition, in face of the complexity of healthcare management problems, specially due to the strong uncertainty inherent to this type of problems, healthcare decision makers need decision support tools to reduce costs without impacting quality of care. In this context, the field of operations research has an extensive set of techniques that have been applied to healthcare management problems. In particular, due to the high volume of resources assigned to the operating theater (OT), the application of operations resources techniques to OT management problems has been an active research area. Nevertheless, it still presents well known research gaps, among them, the lack of efficient and realistic elective surgery scheduling methods. This thesis proposes a decision support system (DSS) for the elective surgery scheduling problem and four progressive more complex scheduling methods. The DSS tackles the issues of decision support, uncertainty reduction and surgery schedule optimization, through the integration of data mining and optimization techniques. This system was designed based on the needs of surgeons and hospital managers from a large hospital in the north of Portugal. Regarding schedule optimization, the first scheduling method, which is integrated into the DSS and is proposed to automate the process of generating new schedules, consists in a mixed integer programming (MIP) model which uses a discrete representation of time. The second method consists in a new MIP formulation using a continuous representation of time that is able to find better solutions in a reduced amount of time. The third method is composed of a genetic algorithm and a set of local search procedures designed to tackle large scale problems. Finally, the last method consists in a new multi-objective optimization approach based on the integration between simulation and optimization to tackle a stochastic version of the problem with multiple sources of uncertainty. This approach is a proactive way to reduce the impact of uncertainty in the execution of the schedules. The proposed DSS and new scheduling methods tackle an important societal issue and are direct contributions to the scientific community, as they allow for increased productivity and efficiency in the elective surgery scheduling processes." @default.
• W650628586 created "2016-06-24" @default.
• W650628586 creator A5038514549 @default.
• W650628586 date "2015-02-20" @default.
• W650628586 modified "2023-09-27" @default.
• W650628586 title "Large scale elective surgery scheduling under uncertainty" @default.
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