Advanced search
1 file | 1.51 MB Add to list

Surgery sequencing to minimize the expected maximum waiting time of emergent patients

Author
Organization
Abstract
This paper investigates the problem of surgery sequencing under uncertain surgery durations, with the objective of minimizing the expected maximum waiting time that emergent patients may endure before an operating room becomes available. Given the sets of surgeries assigned to each operating room as well as the distribution of each surgery’s duration, we aim to find the room sequences that minimize the expected largest interval between consecutive completion times. To solve the resulting stochastic optimization problem called the Stochastic Break-In Moment problem (SBIM), we implement a new MILP based on Sample Average Approximation. An algorithm that more intelligently segments the search further improves performance. We also create a variance-based heuristic and adapt a variety of local search methods to the SBIM case. Numerical experiments based on a surgery scheduling benchmark set show that stochastic solution methods outperform their deterministic counterparts, and highlight the value of the stochastic solution.
Keywords
OR in health services, Operating room scheduling, Emergency arrivals, Uncertain surgery duration, Sample Average Approximation

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.51 MB

Citation

Please use this url to cite or link to this publication:

MLA
Vandenberghe, Mathieu et al. “Surgery Sequencing to Minimize the Expected Maximum Waiting Time of Emergent Patients.” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 275.3 (2019): 971–982. Print.
APA
Vandenberghe, M., De Vuyst, S., Aghezzaf, E.-H., & Bruneel, H. (2019). Surgery sequencing to minimize the expected maximum waiting time of emergent patients. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 275(3), 971–982.
Chicago author-date
Vandenberghe, Mathieu, Stijn De Vuyst, El-Houssaine Aghezzaf, and Herwig Bruneel. 2019. “Surgery Sequencing to Minimize the Expected Maximum Waiting Time of Emergent Patients.” European Journal of Operational Research 275 (3): 971–982.
Chicago author-date (all authors)
Vandenberghe, Mathieu, Stijn De Vuyst, El-Houssaine Aghezzaf, and Herwig Bruneel. 2019. “Surgery Sequencing to Minimize the Expected Maximum Waiting Time of Emergent Patients.” European Journal of Operational Research 275 (3): 971–982.
Vancouver
1.
Vandenberghe M, De Vuyst S, Aghezzaf E-H, Bruneel H. Surgery sequencing to minimize the expected maximum waiting time of emergent patients. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. Elsevier; 2019;275(3):971–82.
IEEE
[1]
M. Vandenberghe, S. De Vuyst, E.-H. Aghezzaf, and H. Bruneel, “Surgery sequencing to minimize the expected maximum waiting time of emergent patients,” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol. 275, no. 3, pp. 971–982, 2019.
@article{8588246,
  abstract     = {This paper investigates the problem of surgery sequencing under uncertain surgery durations, with the objective of minimizing the expected maximum waiting time that emergent patients may endure before an operating room becomes available. Given the sets of surgeries assigned to each operating room as well as the distribution of each surgery’s duration, we aim to find the room sequences that minimize the expected largest interval between consecutive completion times. To solve the resulting stochastic optimization problem called the Stochastic Break-In Moment problem (SBIM), we implement a new MILP based on Sample Average Approximation. An algorithm that more intelligently segments the search further improves performance. We also create a variance-based heuristic and adapt a variety of local search methods to the SBIM case. Numerical experiments based on a surgery scheduling benchmark set show that stochastic solution methods outperform their deterministic counterparts, and highlight the value of the stochastic solution.},
  author       = {Vandenberghe, Mathieu and De Vuyst, Stijn and Aghezzaf, El-Houssaine and Bruneel, Herwig},
  issn         = {0377-2217},
  journal      = {EUROPEAN JOURNAL OF OPERATIONAL RESEARCH},
  keywords     = {OR in health services,Operating room scheduling,Emergency arrivals,Uncertain surgery duration,Sample Average Approximation},
  language     = {eng},
  number       = {3},
  pages        = {971--982},
  publisher    = {Elsevier},
  title        = {Surgery sequencing to minimize the expected maximum waiting time of emergent patients},
  url          = {http://dx.doi.org/10.1016/j.ejor.2018.11.073},
  volume       = {275},
  year         = {2019},
}

Altmetric
View in Altmetric
Web of Science
Times cited: