Research Article
BibTex RIS Cite

Optimization of Nurse Scheduling Problem with Genetic Algorithms and an Application

Year 2021, İstiklal Marşı 100. Yıl Armağan Sayısı, 203 - 210, 19.05.2021
https://doi.org/10.18026/cbayarsos.730727

Abstract

The main purpose of health services are to protect the health of people, families and communities, to treat patients, and to ensure that they can continue their lives in a healthy way. The nurse problem is commonly studied in the health sector and aims to increase the quality of service. The problem is briefly; is the decision-making process for the assignment of limited resources for specific purposes, under certain constraints and within a specified time period. Scheduling problems are among the ones whose solutions are rather difficult. Hence, in this study, Genetic Algorithms, one of the heuristic algorithms, has been used in order to reach approximately the best solution values. The study has been conducted on the data of nurses working in a clinic of Seyfi Demirsoy State Hospital. So as to find the most available work-hours of nurses, the genetic algorithms toolbox of MATLAB is utilized. Finally, the study has been completed by comparing the authentic shift-schedules of the hospital with the attained results.

References

  • Aickelin, U., & Dowsland, K.A. (2004). An indirect genetic algorithm for a nurse-scheduling problem. Computers & Operations Research, 31(5), 761-778.
  • Alfadilla, N., Sentia, P.D., & Asmadi, D. (2019). Optimization of nurse scheduling problem using genetic algorithm: a case study. In IOP Conference Series: Materials Science and Engineering, 536(1), (p. 012131).
  • Bailey, R. N., Garner, K. M., & Hobbs, M. F. (1997). Using simulated annealing and genetic algorithms to solve staff-scheduling problems. Asia-Pacific Journal of Operational Research, 14(2), 27-43.
  • Balekar, S. S., & Mhetre, N. A. (2013). Survey of genetic algorithm approach for nurse scheduling problem. International Journal of Science and Research, 4(6), 55-62.
  • Davis, L. (1985). Job shop scheduling with genetic algorithm. Proceeding of the first International Conference on Genetic Algorithms, 9(5), 136-140.
  • Duenas, A., Tütüncü, G. Y., & Chilcott, J. B. (2009). A genetic algorithm approach to the nurse scheduling problem with fuzzy preferences. IMA Journal of Management Mathematics, 20(4), 369-383.
  • Ernst, A.T., Jiang, H., Krishnamoorthy, M. ve Sier, D. (2004). Staff scheduling and rostering: a review of applications, methods and models. European Journal of Operational Research, 153(1), 3–27.
  • Goldberg, D. E., & Kuo, C. H. (1987). Genetic algorithms in pipeline optimization. Journal of Computing in Civil Engineering, 1(2), 128-141. Inoue, T., Furuhashi, T., Fujii, M., Maeda, H., & Takaba, M. (1999, October). Development of nurse scheduling support system using interactive EA. In IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics, 5, 533-537.
  • İnanç, Ş., & Şenaras, A. E. (2020). Solving nurse scheduling problem via genetic algorithm in home healthcare. In Transportation, Logistics, and Supply Chain Management in Home Healthcare: Emerging Research and Opportunities, 20-28.
  • Jian, M. S., & You, M. S. (2016). Cloud based hybrid evolution algorithm for NP-complete pattern in nurse scheduling problem. International Journal of Innovation, Management and Technology, 7(5), 234-237.
  • Kawanaka, H., Yamamoto, K., Yoshikawa, T., Shinogi, T., & Tsuruoka, S. (2001). Genetic algorithm with the constraints for nurse scheduling problem. In Proceedings of the 2001 Congress on Evolutionary Computation, 2, 1123-1130.
  • Kim, J., Jeon, W., Ko, Y. W., Uhmn, S., & Kim, D. H. (2018). Genetic local search for nurse scheduling problem. Advanced Science Letters, 24(1), 608-612.
  • Leksakul, K., & Phetsawat, S. (2014). Nurse scheduling using genetic algorithm. Mathematical Problems in Engineering, 2014.
  • Moz, M., & Pato, M. V. (2007). A genetic algorithm approach to a nurse rerostering problem. Computers & Operations Research, 34(3), 667-691.
  • Musa, A. A., & Saxena, U. (1984). Scheduling nurses using goal-programming techniques. IIE transactions, 16(3), 216-221.
  • Obitko, M. (1998), Introduction to Genetic Algorithms with Java Applets, 19 Nisan 2016 tarihinde http://www.obitko.com/tutorials/genetic-algorithms/introduction.php adresinden alındı.
  • Reeves, C. (2003). Genetic algorithms. In Handbook of metaheuristics (p. 55-82), Boston: Springer.
  • Smith, L. D., & Wiggins, A. (1977). A computer-based nurse scheduling system. Computers & Operations Research, 4(3), 195-212.
  • Taylor, A. M. (1940). A staff nurse program. The American Journal of Nursing, 40(2), 137-145.
  • Tsai, C. C., & Li, S. H. (2009). A two-stage modeling with genetic algorithms for the nurse scheduling problem. Expert Systems with Applications, 36(5), 9506-9512.
  • Yamamura, M., Kobayash, S., Yamagishi, M., & Ase, H. (1993). Nurse scheduling by genetic algorithms. Hiroaki Kitano: Genetic Algorithms, 2, 89-125.
  • Wolfe, H., & Young, J. P. (1965). Staffing the nursing unit: Part I. controlled variable staffing. Nursing Research, 14(3), 236-242.
  • Wolfe, H., & Young, J. P. (1965). Staffing the nursing unit part II. The multiple assignment technique. Nursing Research, 14(4), 299-303.

Hemşire Çizelgeleme Problemlerinin Genetik Algoritmalarla Optimizasyonu ve Bir Uygulama

Year 2021, İstiklal Marşı 100. Yıl Armağan Sayısı, 203 - 210, 19.05.2021
https://doi.org/10.18026/cbayarsos.730727

Abstract

Sağlık hizmetlerinin temel amacı; kişi, aile ve toplumların sağlıklarının korunması, hastaların tedavi edilmesi ve yaşamlarını sağlıklı olarak sürdürebilmelerini sağlamaktır. Hemşire Çizelgeleme Problemi, sağlık sektöründe çok çalışılan ve hizmet kalitesini artırmayı hedefleyen bir problemdir. Problem kısaca; sınırlı kaynakların belirli amaçlar doğrultusunda, belirli kısıtlar altında ve belirli bir zaman aralığında işlere atanması ile ilgili karar verme sürecini inceler. Çizelgeleme problemleri, çözümü zor olan problemler grubuna girmektedir. Bu yüzden bu çalışmada yaklaşık en iyi çözüm değerlerine ulaşabilmek için sezgisel algoritmalardan biri olan Genetik Algoritmalar kullanılmıştır. Çalışma, Buca Seyfi Demirsoy Devlet Hastanesi’nin bir servisindeki hemşirelerin verileriyle yapılmıştır. Hemşirelerin en uygun çalışma saatlerini bulmak amacıyla oluşturulan modelin çözümü için MATLAB programının genetik algoritmalar aracından yararlanılmıştır. Son olarak elde edilen sonuçlarla hastanenin gerçek nöbet çizelgeleri karşılaştırılarak çalışma tamamlanmıştır.

References

  • Aickelin, U., & Dowsland, K.A. (2004). An indirect genetic algorithm for a nurse-scheduling problem. Computers & Operations Research, 31(5), 761-778.
  • Alfadilla, N., Sentia, P.D., & Asmadi, D. (2019). Optimization of nurse scheduling problem using genetic algorithm: a case study. In IOP Conference Series: Materials Science and Engineering, 536(1), (p. 012131).
  • Bailey, R. N., Garner, K. M., & Hobbs, M. F. (1997). Using simulated annealing and genetic algorithms to solve staff-scheduling problems. Asia-Pacific Journal of Operational Research, 14(2), 27-43.
  • Balekar, S. S., & Mhetre, N. A. (2013). Survey of genetic algorithm approach for nurse scheduling problem. International Journal of Science and Research, 4(6), 55-62.
  • Davis, L. (1985). Job shop scheduling with genetic algorithm. Proceeding of the first International Conference on Genetic Algorithms, 9(5), 136-140.
  • Duenas, A., Tütüncü, G. Y., & Chilcott, J. B. (2009). A genetic algorithm approach to the nurse scheduling problem with fuzzy preferences. IMA Journal of Management Mathematics, 20(4), 369-383.
  • Ernst, A.T., Jiang, H., Krishnamoorthy, M. ve Sier, D. (2004). Staff scheduling and rostering: a review of applications, methods and models. European Journal of Operational Research, 153(1), 3–27.
  • Goldberg, D. E., & Kuo, C. H. (1987). Genetic algorithms in pipeline optimization. Journal of Computing in Civil Engineering, 1(2), 128-141. Inoue, T., Furuhashi, T., Fujii, M., Maeda, H., & Takaba, M. (1999, October). Development of nurse scheduling support system using interactive EA. In IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics, 5, 533-537.
  • İnanç, Ş., & Şenaras, A. E. (2020). Solving nurse scheduling problem via genetic algorithm in home healthcare. In Transportation, Logistics, and Supply Chain Management in Home Healthcare: Emerging Research and Opportunities, 20-28.
  • Jian, M. S., & You, M. S. (2016). Cloud based hybrid evolution algorithm for NP-complete pattern in nurse scheduling problem. International Journal of Innovation, Management and Technology, 7(5), 234-237.
  • Kawanaka, H., Yamamoto, K., Yoshikawa, T., Shinogi, T., & Tsuruoka, S. (2001). Genetic algorithm with the constraints for nurse scheduling problem. In Proceedings of the 2001 Congress on Evolutionary Computation, 2, 1123-1130.
  • Kim, J., Jeon, W., Ko, Y. W., Uhmn, S., & Kim, D. H. (2018). Genetic local search for nurse scheduling problem. Advanced Science Letters, 24(1), 608-612.
  • Leksakul, K., & Phetsawat, S. (2014). Nurse scheduling using genetic algorithm. Mathematical Problems in Engineering, 2014.
  • Moz, M., & Pato, M. V. (2007). A genetic algorithm approach to a nurse rerostering problem. Computers & Operations Research, 34(3), 667-691.
  • Musa, A. A., & Saxena, U. (1984). Scheduling nurses using goal-programming techniques. IIE transactions, 16(3), 216-221.
  • Obitko, M. (1998), Introduction to Genetic Algorithms with Java Applets, 19 Nisan 2016 tarihinde http://www.obitko.com/tutorials/genetic-algorithms/introduction.php adresinden alındı.
  • Reeves, C. (2003). Genetic algorithms. In Handbook of metaheuristics (p. 55-82), Boston: Springer.
  • Smith, L. D., & Wiggins, A. (1977). A computer-based nurse scheduling system. Computers & Operations Research, 4(3), 195-212.
  • Taylor, A. M. (1940). A staff nurse program. The American Journal of Nursing, 40(2), 137-145.
  • Tsai, C. C., & Li, S. H. (2009). A two-stage modeling with genetic algorithms for the nurse scheduling problem. Expert Systems with Applications, 36(5), 9506-9512.
  • Yamamura, M., Kobayash, S., Yamagishi, M., & Ase, H. (1993). Nurse scheduling by genetic algorithms. Hiroaki Kitano: Genetic Algorithms, 2, 89-125.
  • Wolfe, H., & Young, J. P. (1965). Staffing the nursing unit: Part I. controlled variable staffing. Nursing Research, 14(3), 236-242.
  • Wolfe, H., & Young, J. P. (1965). Staffing the nursing unit part II. The multiple assignment technique. Nursing Research, 14(4), 299-303.
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Ahsen Küçük 0000-0002-3799-100X

İpek Deveci Kocakoç 0000-0001-9155-8269

Publication Date May 19, 2021
Published in Issue Year 2021 İstiklal Marşı 100. Yıl Armağan Sayısı

Cite

APA Küçük, A., & Deveci Kocakoç, İ. (2021). Hemşire Çizelgeleme Problemlerinin Genetik Algoritmalarla Optimizasyonu ve Bir Uygulama. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 19(Armağan Sayısı), 203-210. https://doi.org/10.18026/cbayarsos.730727