A Multi-objective Hospital Operating Room Planning and Scheduling Problem Using Compromise Programming
Christine Di Martinelly,
G. Yazgi Tütüncü,
Joaquin Aguado and
Alejandra Duenas ()
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This paper proposes a hybrid compromise programming local search approach with two main characteristics: a capacity to generate non-dominated solutions and the ability to interact with the decision maker. Compromise programming is an approach where it is not necessary to determine the entire set of Pareto-optimal solutions but only some of them. These solutions are called compromise solutions and represent a good tradeoff between conflicting objectives. Another advantage of this type of method is that it allows the inclusion of the decision maker's preferences through the definition of weights included in the different metrics used by the method. This approach is tested on an operating room planning process. This process incorporates the operating rooms and the nurse planning simultaneously. Three different objectives were considered: to minimize operating room costs, to minimize the maximum number of nurses needed to participate in surgeries and to minimize the number of open operating rooms. The results show that it is a powerful decision tool that enables the decision makers to apply compromise alongside optimal solutions during an operating room planning process.
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Published in Grigori Sidorov (éd.); Oscar Herrera-Alcántara (éd.). Advances in Computational Intelligence, Springer, pp.379-390, 2017, ⟨10.1007/978-3-319-62434-1_31⟩
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03272706
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