Uma Abordagem Multiobjetivo para o Problema do Escalonamento de Médicos

• Lucas Machado Cid UFSCar
• Mário César San Felice UFSCar
• Pedro H. Del Bianco Hokama UNIFEI

DOI: https://doi.org/10.5753/sbcas.2023.230040

Abstract

The Physician Rostering Problem (PRP) seeks to assign shifts to physicians, so that all hospital demands are met, no physician is overloaded, and the scheduling is as pleasant as possible for them. Constraint Programming (CP) is a paradigm for solving combinatorial problems, which combines techniques from Artificial Intelligence, Theory of Computation and Operations Research. This work uses CP to solve the PRP considering demands from a real hospital, while addresses the quality and variety of solutions from the perspective of multiobjective optimization in order to assist the decision maker. Satisfactory results have been achieved for instances with up to 40 physicians and a 30-day planning horizon.

References

Bovet, D. P. and Crescenzi, P. (1994). Introduction to the Theory of Complexity. Prentice Hall.

Brunner, J., Bard, J., and Kolisch, R. (2009). Flexible shift scheduling of physicians. Health Care Management Science, 12:285–305.

Coello, C., Dhaenens, C., and Jourdan, L. (2009). Advances in Multi-Objective Nature Inspired Computing, volume 272. Springer Berlin, Heidelberg.

Dechter, R. (2003). Constraint Processing. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Erhard, M., Schoenfelder, J., Fügener, A., and Brunner, J. O. (2018). State of the art in physician scheduling. European Journal of Operational Research, 265(1):1–18.

Fügener, A., Brunner, J. O., and Podtschaske, A. (2015). Duty and workstation rostering considering preferences and fairness: a case study at a department of anaesthesiology. International Journal of Production Research, 53(24):7465–7487.

Rossi, F., Beek, P. v., and Walsh, T. (2006). Handbook of Constraint Programming (Foundations of Artificial Intelligence). Elsevier Science Inc., USA.