Skip to main content

Advertisement

SpringerLink
Log in

ACO for the Surgical Cases Assignment Problem

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This paper addresses the Surgical Case Assignment Problem with an objective of minimizing the total unexploited and operating cost. A two-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of Branch and Bound and a global solver. The results show that ACO outperformed the other algorithms and reached better solutions in a faster computational time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. ACOforSCAP., Accessed October 09, 2010 from http://sites.google.com/site/jparnaout/acoforscap, 2010.

  2. Arnaout, J.-P., Heuristics for the maximization of Operating Rooms utilization using Simulation. Simulation. 86:573–583, 2010.

    Article  Google Scholar 

  3. Arnaout, J.-P., Rabadi, G., and Musa, R., A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times. J. Intell. Manuf. 21:693–701, 2010.

    Article  Google Scholar 

  4. Blake, J. T., and Donald, J., Mount Sinai hospital uses integer-programming to allocate operating room time. Interfaces. 32(2):63–73, 2002.

    Article  Google Scholar 

  5. Cardoen, B., Demeulemeester, E., and Belien, J., Operating room planning and scheduling: A literature review. Eur. J. Oper. Res. 201:921–932, 2010.

    Article  MATH  Google Scholar 

  6. Clergue, F., Gestion du bloc opératoire: Pourquoi une telle préoccupation? Informations cliniques en Anesthésie-Réanimation, 93–95, 1999.

  7. Dexter, F., Macario, A., Traub, R., Hopwood, M., and Lubarsky, D., 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. 89:7–20, 1999.

    Google Scholar 

  8. Dexter, F., A strategy to decide whether to move the last case of the day in an operating room to another empty operating room to decrease overtime labor costs. Anesth. Analg. 91:925–928, 2000.

    Article  Google Scholar 

  9. Dorigo, M., Optimization, learning and natural algorithms, PhD thesis, Politecnico di Milano, Italie, 1992.

  10. Dorigo, M., and Stützle, T., Ant colony optimization. MIT Press, 2004.

  11. Fei, H., Chu, C., Meskens, N., and Artiba, A., Solving surgical cases assignment problem by a branch-and-price approach. Int. J. Prod. Econ. 112:96–108, 2008.

    Article  Google Scholar 

  12. Fisher, R. A., The design of experiments. Hafner Publishing Company, New York, 1960.

    Google Scholar 

  13. Guinet, A., and Chaabane, S., Operating theatre planning. Int. J. Prod. Econ. 85:69–81, 2003.

    Article  Google Scholar 

  14. Jebali, A., Alouane, A., and Ladet, P., Operating rooms scheduling. Int. J. Prod. Econ. 99:52–62, 2006.

    Article  Google Scholar 

  15. Lindo Systems., Accessed October 09, 2010 from http://www.lindo.com, 2010.

  16. Kharraja, S., Chaabane, S., and Marcon, E., Evaluation de performances pour deux stratégies de programmation opératoire de bloc. In: Actes de la 2 eme Conf Int Francophone d’Automatique, Nantes, France, 2002.

  17. NIST/SEMATECH e-Handbook of Statistical Methods. Accessed October 09, 2010 from http://www.itl.nist.gov/div898/handbook/.

  18. Ross, P., Taguchi techniques for quality engineering. McGraw Hill, NewYork, 1996.

    Google Scholar 

  19. Shmitz, H., and Kwak, N., Monte Carlo simulation of operating-room and recovery-room usage. Oper. Res. 20:1171–1180, 1972.

    Article  Google Scholar 

  20. Taguchi, G., Taguchi methods: Design of experiments. American Supplier Institute, Inc, Michigan, 1993.

    Google Scholar 

  21. Tsoy, G., Arnaout, J-P., Smith, T., and Rabadi, G., A genetic algorithm approach for surgery operating rooms scheduling problem. In: Proceedings of the 25th National Conference of the American Society for Engineering Management, Alexandria, Virginia. 2004.

  22. Vissers, J., Patient flow-based allocation of inpatient resources: A case study. Eur. J. Oper. Res. 105:356–370, 1998.

    Article  MATH  Google Scholar 

  23. Weinbroum, A. A., Ekstein, P., and Ezri, T., Efficiency of the operating room suite. Am. J. Surg. 185:244–250, 2003.

    Article  Google Scholar 

  24. Weng, M., Lu, J., and Ren, H., Unrelated parallel machine scheduling with setup consideration and a total weighted completion time objective. Int. J. Prod. Econ. 70:215–226, 2001.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial and Mechanical Engineering Department, Lebanese American University, P.O. Box 36, Byblos, 101-F, Lebanon

    Charbel Rizk & Jean-Paul Arnaout

Authors
  1. Charbel Rizk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Paul Arnaout
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean-Paul Arnaout.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rizk, C., Arnaout, JP. ACO for the Surgical Cases Assignment Problem. J Med Syst 36, 1891–1899 (2012). https://doi.org/10.1007/s10916-010-9648-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9648-z

Keywords

Search

Navigation