Advertisement

A hybrid genetic algorithm for operating room scheduling

  • Yang-Kuei LinEmail author
  • Yin-Yi Chou
Article
  • 74 Downloads

Abstract

In this research, we studied operating room scheduling problem of assigning a set of surgeries to several multifunctional operating rooms. The objectives are to maximize the utilization of the operating rooms, to minimize the overtime-operating cost, and to minimize the wasting cost for the unused time. To begin with, a revised mathematical model is constructed to assign surgeries to operating rooms within one week. Then, we proposed four easy-to-implement heuristics that can guarantee to find feasible solutions for the studied problem efficiently. Furthermore, we presented four local search procedures that can improve a given solution significantly. Finally, a hybrid genetic algorithm (HGA) that incorporated with initial solutions, local search procedures and elite search procedure is applied to the studied problem. Computational results show that for small problem instances, the HGA can find near optimal solutions efficiently while for large problem instances, the HGA performs significantly better than the four proposed heuristics. We concluded that surgery schedules obtained by using HGA has less wasting cost for the unused time, much higher utilization of operating rooms, and produce less overtime-operating cost.

Keywords

Scheduling Operating rooms Heuristics Genetic algorithm 

Notes

References

  1. 1.
    Gordon T, Lyles A, Fountain J, Paul S (1988) Surgical unit time utilization review: resource utilization and management implications. J Med Syst 12(3):169–179Google Scholar
  2. 2.
    HFMA (2003) Achieving operating room efficiency through process integration. Healthc Financ Manag 57(3): suppl-1Google Scholar
  3. 3.
    Cardoen B, Demeulemeester E, Beliën J (2010) Operating room planning and scheduling: a literature review. Eur J Oper Res 201(3):921–932Google Scholar
  4. 4.
    Latorre-Núñez G, Lüer-Villagra A, Marianov V, Obreque C, Ramis F, Neriz L (2016) Scheduling operating rooms with consideration of all resources, post anesthesia beds and emergency surgeries. Comput Ind Eng 97:248–257Google Scholar
  5. 5.
    Dexter F, Macario A, Traub RD, Hopwood M, Lubarsky DA (1999) An operating room scheduling strategy to maximize the use of operating room block time: computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time. Anesth Analg 89(1):7–20Google Scholar
  6. 6.
    Dexter F, Macario A, Traub R (1999) Which algorithm for scheduling add-on elective cases maximizes operating room utilization? use of bin packing algorithms and fuzzy constraints in operating room management. Anesthesiology 91(5):1491–1500Google Scholar
  7. 7.
    Guinet A, Chaabane S (2003) Operating theatre planning. Int J Prod Econ 85(1):69–81Google Scholar
  8. 8.
    Fei H, Meskens N, Chu C (2006) An operating theatre planning and scheduling problem in the case of a "block scheduling" strategy. In: International conference on service systems and service management. IEEE, (1): 422–428Google Scholar
  9. 9.
    Fei H, Combes C, Meskens N, Chu C (2006) Endoscopies scheduling problem: a case study. In: Procceding of 12th IFAC symposium on information control problems in manufacturing (INCOM06), 39(3): 635–640Google Scholar
  10. 10.
    Fei H, Chu C, Meskens N, Artiba A (2008) Solving surgical cases assignment problem by a branch-and-price approach. Int J Prod Econ 112(1):96–108Google Scholar
  11. 11.
    Fei H, Chu C, Meskens N (2009) Solving a tactical operating room planning problem by a column-generation-based heuristic procedure with four criteria. Ann Oper Res 166(1):91–108Google Scholar
  12. 12.
    Fei H, Meskens N, Chu C (2010) A planning and scheduling problem for an operating theatre using an open scheduling strategy. Comput Ind Eng 58(2):221–230Google Scholar
  13. 13.
    Liu Y, Chu C, Wang K (2011) A new heuristic algorithm for the operating room scheduling problem. Comput Ind Eng 61(3):865–871Google Scholar
  14. 14.
    Rizk C, Arnaout JP (2012) ACO for the surgical cases assignment problem. J Med Syst 36(3):1891–1899Google Scholar
  15. 15.
    Roland B, Di Martinelly C, Riane F, Pochet Y (2010) Scheduling an operating theatre under human resource constraints. Comput Ind Eng 58(2):212–220Google Scholar
  16. 16.
    Vijayakumar B, Parikh PJ, Scott R, Barnes A, Gallimore J (2013) A dual bin-packing approach to scheduling surgical cases at a publicly-funded hospital. Eur J Oper Res 224(3):583–591Google Scholar
  17. 17.
    Marques I, Captivo ME, Pato MV (2014) Scheduling elective surgeries in a Portuguese hospital using a genetic heuristic. Oper Res Health Care 3(2):59–72Google Scholar
  18. 18.
    Aringhieri R, Landa P, Soriano P, Tanfani E, Testi A (2015) A two level metaheuristic for the operating room scheduling and assignment problem. Comput Oper Res 54:21–34Google Scholar
  19. 19.
    Hashemi Doulabi SH, Rousseau LM, Pesant G (2016) A constraint-programming-based branch-and-price-and-cut approach for operating room planning and scheduling. INFORMS J Comput 28(3):432–448Google Scholar
  20. 20.
    Roshanaei V, Luong C, Aleman DM, Urbach D (2017) Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling. Eur J Oper Res 257(2):439–455Google Scholar
  21. 21.
    Blake JT, Donald J (2002) Mount sinai hospital uses integer programming to allocate operating room time. Interfaces 32(2):63–73Google Scholar
  22. 22.
    Jebali A, Alouane ABH, Ladet P (2006) Operating rooms scheduling. Int J Prod Econ 99(1–2):52–62Google Scholar
  23. 23.
    Graham RL (1969) Bounds on multiprocessing timing anomalies. SIAM J Appl Math 17(2):416–429Google Scholar
  24. 24.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  25. 25.
    Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, BostonGoogle Scholar
  26. 26.
    Mitchell M (1998) An introduction to genetic algorithms. MIT press, CambridgeGoogle Scholar
  27. 27.
    Vallada E, Ruiz R (2011) A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. Eur J Oper Res 211(3):612–622Google Scholar
  28. 28.
    Lin YK, Pfund ME, Fowler JW (2011) Heuristics for minimizing regular performance measures in unrelated parallel machine scheduling problems. Comput Oper Res 38(6):901–916Google Scholar
  29. 29.
    Lin YK, Fowler JW, Pfund ME (2013) Multiple-objective heuristics for scheduling unrelated parallel machines. Eur J Oper Res 227(2):239–253Google Scholar
  30. 30.
    Lee WC, Wang JY, Lee LY (2015) A hybrid genetic algorithm for an identical parallel-machine problem with maintenance activity. J Oper Res Soc 66(11):1906–1918Google Scholar
  31. 31.
    Gen M, Cheng R (1996) Genetic algorithm and engineering design. Wiley, HobokenGoogle Scholar
  32. 32.
    Cheng R, Gen M, Tosawa T (1995) Minmax earliness/ tardiness scheduling in identical parallel machine system using genetic algorithms. Comput Ind Eng 29(1–4):513–517Google Scholar
  33. 33.
    Lin YK (2013) Particle swarm optimization algorithm for unrelated parallel machine scheduling with release dates. Math Probl Eng:1–9Google Scholar
  34. 34.
    Lin YK, Hsieh FY (2013) Unrelated parallel machine scheduling with setup times and ready times. Int J Prod Res 52(4):1200–1214Google Scholar
  35. 35.
    Kerkhove LP, Vanhoucke M (2014) Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics. Int J Prod Res 52(9):2630–2653Google Scholar
  36. 36.
    Zarandi MF, Kayvanfar V (2015) A bi-objective identical parallel machine scheduling problem with controllable processing times: a just-in-time approach. Int J Adv Manuf Technol 77(1–4):545–563Google Scholar
  37. 37.
    Bagchi TP (1999) Multiobjective scheduling by genetic algorithms. Kluwer Academic Publishers, NorwellGoogle Scholar
  38. 38.
    Montgomery DC (2017) Design and analysis of experiments, 9th edn. Wiley, HobokenGoogle Scholar
  39. 39.
    Myers RH, Montgomery DC (2016) Response surface methodology: process and product optimization using designed experiments, 4th edn. Wiley, HobokenGoogle Scholar
  40. 40.
    Weng MX, Lu J, Ren H (2001) Unrelated parallel machine scheduling with setup consideration and a total weighted completion time objective. Int J Prod Econ 70(3):215–226Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Systems ManagementFeng Chia UniversityTaichungRepublic of China

Personalised recommendations