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Nurse Scheduling Using Genetic Algorithm

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  • Komgrit Leksakul
  • Sukrit Phetsawat

Abstract

This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital-staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA) with two-point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

Suggested Citation

  • Komgrit Leksakul & Sukrit Phetsawat, 2014. "Nurse Scheduling Using Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-16, November.
    • Handle: RePEc:hin:jnlmpe:246543
    DOI: 10.1155/2014/246543
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    Cited by:

    1. Junhong Guo & William Pozehl & Amy Cohn, 2023. "A two-stage partial fixing approach for solving the residency block scheduling problem," Health Care Management Science, Springer, vol. 26(2), pages 363-393, June.

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