MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING

Ladislav Rosocha (1), Silvia Vernerova (2), Robert Verner (3)
(1) University of Economics in Bratislava, Faculty of Business Economy with seat in Košice, Slovakia,
(2) Louis Pasteur University Hospital, Slovakia,
(3) University of Economics in Bratislava, Faculty of Business Economy with seat in Košice, Slovakia

Abstract

Purpose: The efficiency of medical staff is a fundamental feature of healthcare facilities quality. Therefore the better implementation of their preferences into the scheduling problem might not only rise the work-life balance of doctors and nurses, but also may result into better patient care. This paper focuses on optimization of medical staff preferences considering the scheduling problem.

Methodology/Approach: We propose a medical staff scheduling algorithm based on simulated annealing, a well-known method from statistical thermodynamics. We define hard constraints, which are linked to legal and working regulations, and minimize the violations of soft constraints, which are related to the quality of work, psychic, and work-life balance of staff.

Findings: On a sample of 60 physicians and nurses from gynecology department we generated monthly schedules and optimized their preferences in terms of soft constraints. Our results indicate that the final value of objective function optimized by proposed algorithm is more than 18-times better in violations of soft constraints than initially generated random schedule that satisfied hard constraints.

Research Limitation/implication: Even though the global optimality of final outcome is not guaranteed, desirable solutionwas obtained in reasonable time.

Originality/Value of paper: We show that designed algorithm is able to successfully generate schedules regarding hard and soft constraints. Moreover, presented method is significantly faster than standard schedule generation and is able to effectively reschedule due to the local neighborhood search characteristics of simulated annealing.

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References

Aarts, E., Korst, J. and Michiels, W., 2007. Theoretical aspects of local search. Berlin: Springer, ISBN 978-3-540-35853-4.

Albrecht, A., Steinhöfel, K., Taupitz, M. and Wong, C.K., 2001. Logarithmic simulated annealing for X-ray diagnosis. Artificial intelligence in medicine, 22(3), pp.249-260.

Bruni, R. and Detti, P., 2014. A flexible discrete optimization approach to the physician scheduling problem. Operations Research for Health Care, 3(4), pp.191-199.

Černý, V., 1985. Thermodynamical approach to the Traveling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), pp.41-51.

Chern, C.C., Chien, P.S. and Chen, S.Y., 2008. A heuristic algorithm for the hospital health examination scheduling problem. European Journal of Operational Research, 186(3), pp.1137-1157.

Crama, Y. and Schyns, M., 2003. Simulated annealing for complex portfolio selection problems. European Journal of operational research, 150(3), pp.546-571.

Demeester, P., Souffriau, W., De Causmaecker, P. and Vanden Berghe, G., 2010. A hybrid tabu search algorithm for automatically assigning patients to beds. Artificial Intelligence in Medicine, 48(1), pp.61-70.

Fei, H., Meskens, N. and Chu, C., 2010. A planning and scheduling problem for an operating theatre using an open scheduling strategy. Computers & Industrial Engineering, 58(2), pp.221-230.

Heragu, S.S., Alfa, A.S., 1992. Experimental analysis of simulated annealing based algorithms for the layout problem. European Journal of Operational Research, 57(2), pp.190-202.

Jacob, D. et al., 2008. Anatomy-based inverse planning simulated annealing optimization in high-dose-rate prostate brachytherapy: significant dosimetric advantage over other optimization techniques. International Journal of Radiation Oncology* Biology* Physics, 72(3), pp.820-827.

Kirkpatrick, PP., Gelatt, C.D. and Vecchi, M., 1983. Optimization by simulated annealing. Science, 220(4598), pp.671-680.

Koulamas, C., Antony, S.R. and Jaen, R., 1994. A survey of simulated annealing applications to operations research problems. Omega, 22(1), pp.41-56.

Liu, Y., Chu, C. and Wang, K., 2011. A new heuristic algorithm for the operating room scheduling problem. Computers & Industrial Engineering, 61(3), pp.865-871.

Luo, Y., Zhu, B. and Tang, Y., 2014. Simulated annealing algorithm for optimal capital growth. Physica A: Statistical Mechanics and its Applications, 408, pp.10-18.

Marques, I., Captivo, M.E. and Vaz Pato, M., 2014. Scheduling elective surgeries in a Portuguese hospital using a genetic heuristic. Operations Research for Health Care, 3(2), pp.59-72.

Metropolis, N. et al., 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21(6), pp.1087-1092.

Range, T.M., Lusby, R.M. and Larsen, J., 2014. A column generation approach for solving the patient admission scheduling problem. European Journal of Operational Research, 235(1), pp.252-264.

Riise, A. and Burke, E.K., 2011. Local search for the surgery admission planning problem. Journal of Heuristics, 17(4), pp.389-414.

Souilah, A., 1995. Simulated annealing for manufacturing systems layout design. European Journal of Operational Research, 82(3), pp.592-614.

Szabo, S., Ferencz, V. and Pucihar, A., 2013. A. Trust, innovation and prosperity. Quality Innovation Prosperity, 17(2), pp.1-8.

Van Breedam, A., 1995. Improvement heuristics for the vehicle routing problem based on simulated annealing. European Journal of Operational Research, 86(3), pp.480-490.

Vijayakumar, B. et al., 2013. A dual bin-packing approach to scheduling surgical cases at a publicly-funded hospital. European Journal of Operational Research, 224(3), pp.583-591.

Wong, T.C., Xu, M. and Chin, K.S., 2014. A two-stage heuristic approach for nurse scheduling problem: A case study in an emergency department. Computers & Operations Research, 51, pp.99-110.

Zarandi, M.H., Zarinbal, M., Ghanbari, N. and Turksen, I.B., 2013. A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction. Information Sciences, 222 (10 February), pp.213-228.

Authors

Ladislav Rosocha
unlp@unlp.sk (Primary Contact)
Silvia Vernerova
Robert Verner
Rosocha, L., Vernerova, S., & Verner, R. (2014). MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING. Quality Innovation Prosperity, 19(1), 1–8. https://doi.org/10.12776/qip.v19i1.405

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