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Marques, I., M.E. Captivo & Pato, M.V. (2014). Bicriteria elective surgery scheduling using an evolutionary algorithm. For Better Practices in Health Care Management.

I. M. Proença et al.,  "Bicriteria elective surgery scheduling using an evolutionary algorithm", in For Better Practices in Health Care Management, Lisboa, 2014

@misc{proença2014_1775206733518,
	author = "Marques, I. and M.E. Captivo and Pato, M.V.",
	title = "Bicriteria elective surgery scheduling using an evolutionary algorithm",
	year = "2014",
	howpublished = "Outro",
	url = ""
}

TY  - CPAPER
TI  - Bicriteria elective surgery scheduling using an evolutionary algorithm
T2  - For Better Practices in Health Care Management
AU  - Marques, I.
AU  - M.E. Captivo
AU  - Pato, M.V.
PY  - 2014
CY  - Lisboa
AB  - Current social and economic environment forces better practices among health care service organizations. Portuguese National Health Plan outlines the urgency of improving the efficiency of the health care systems’ installed capacity, and reducing the waiting lists for surgery. Hence this work is dedicated to a case study of an elective surgery scheduling problem arising in a Lisbon public hospital. In order to increase the surgical suite’s efficiency and reduce the waiting lists for surgery, two conflicting objectives are therefore considered: maximize surgical suite occupation and maximize the number of surgeries scheduled. This elective surgery scheduling problem consists of assigning an intervention date, an operating room and a starting time for elective surgeries selected from the hospital waiting list. To obtain potentially non-dominated solutions to this bicriteria problem an evolutionary algorithm is presented. The algorithm uses an indirect representation and randomly generates weights for each objective in order to decode each cromossome into a complete solution. The elitist strategy and the structure of the evolutive process are based on a biased crowded tournament selection and a parametrized uniform crossover. Tests performed using hospital real data showed a good performance of the algorithm proposed: instances with 508 to 2,306 elective surgeries are solved in less than 80 seconds. A very good representation of the Pareto front was achieved thus providing the opportunity to improve the decision-making process at the hospital. This study presents a powerful method with potential to improve the delivery of surgical activity in the hospital under study.
ER  -