# A bicriteria heuristic for an elective surgery scheduling problem

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## Abstract

Resource rationalization and reduction of waiting lists for surgery are two main guidelines for hospital units outlined in the Portuguese National Health Plan. This work is dedicated to an elective surgery scheduling problem arising in a Lisbon public hospital. In order to increase the surgical suite’s efficiency and to reduce the waiting lists for surgery, two objectives are 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. Accordingly, a bicriteria surgery scheduling problem arising in the hospital under study is presented. To search for efficient solutions of the bicriteria optimization problem, the minimization of a weighted Chebyshev distance to a reference point is used. A constructive and improvement heuristic procedure specially designed to address the objectives of the problem is developed and results of computational experiments obtained with empirical data from the hospital are presented. This study shows that by using the bicriteria approach presented here it is possible to build surgical plans with very good performance levels. This method can be used within an interactive approach with the decision maker. It can also be easily adapted to other hospitals with similar scheduling conditions.

## Keywords

Health care Operating rooms Elective case scheduling Bicriteria optimization Heuristics## Notes

### Acknowledgments

The authors would like to thank Dr. Manuel Delgado for his enthusiastic interest in this work, Dra. Margarida Baltazar for her patient provision of all necessary data and head nurse Fátima Menezes for the friendly and patient description of the entire process. This research is supported by Portuguese National Funding from FCT - Fundação para a Ciência e a Tecnologia, under the project: PEst-OE/MAT/UI0152.

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