TY - JOUR
T1 - Models, algorithms and performance analysis for adaptive operating room scheduling
AU - Xiao, Guanlian
AU - van Jaarsveld, Willem
AU - Dong, Ming
AU - van de Klundert, Joris
N1 - Publisher Copyright:
© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2018/2/16
Y1 - 2018/2/16
N2 - The complex optimisation problems arising in the scheduling of operating rooms have received considerable attention in recent scientific literature because of their impact on costs, revenues and patient health. For an important part, the complexity stems from the stochastic nature of the problem. In practice, this stochastic nature often leads to schedule adaptations on the day of schedule execution. While operating room performance is thus importantly affected by such adaptations, decision-making on adaptations is hardly addressed in scientific literature. Building on previous literature on adaptive scheduling, we develop adaptive operating room scheduling models and problems, and analyse the performance of corresponding adaptive scheduling policies. As previously proposed (fully) adaptive scheduling models and policies are infeasible in operating room scheduling practice, we extend adaptive scheduling theory by introducing the novel concept of committing. Moreover, the core of the proposed adaptive policies with committing is formed by a new, exact, pseudo-polynomial algorithm to solve a general class of stochastic knapsack problems. Using these theoretical advances, we present performance analysis on practical problems, using data from existing literature as well as real-life data from the largest academic medical centre in The Netherlands. The analysis shows that the practically feasible, basic, 1-level policy already brings substantial and statistically significant improvement over static policies. Moreover, as a rule of thumb, scheduling surgeries with large mean duration or standard deviation early appears good practice.
AB - The complex optimisation problems arising in the scheduling of operating rooms have received considerable attention in recent scientific literature because of their impact on costs, revenues and patient health. For an important part, the complexity stems from the stochastic nature of the problem. In practice, this stochastic nature often leads to schedule adaptations on the day of schedule execution. While operating room performance is thus importantly affected by such adaptations, decision-making on adaptations is hardly addressed in scientific literature. Building on previous literature on adaptive scheduling, we develop adaptive operating room scheduling models and problems, and analyse the performance of corresponding adaptive scheduling policies. As previously proposed (fully) adaptive scheduling models and policies are infeasible in operating room scheduling practice, we extend adaptive scheduling theory by introducing the novel concept of committing. Moreover, the core of the proposed adaptive policies with committing is formed by a new, exact, pseudo-polynomial algorithm to solve a general class of stochastic knapsack problems. Using these theoretical advances, we present performance analysis on practical problems, using data from existing literature as well as real-life data from the largest academic medical centre in The Netherlands. The analysis shows that the practically feasible, basic, 1-level policy already brings substantial and statistically significant improvement over static policies. Moreover, as a rule of thumb, scheduling surgeries with large mean duration or standard deviation early appears good practice.
KW - adaptive scheduling
KW - operating room scheduling
KW - operations research in healthcare
KW - stochastic knapsack problem
UR - http://www.scopus.com/inward/record.url?scp=85021142503&partnerID=8YFLogxK
U2 - 10.1080/00207543.2017.1328140
DO - 10.1080/00207543.2017.1328140
M3 - Article
AN - SCOPUS:85021142503
SN - 0020-7543
VL - 56
SP - 1389
EP - 1413
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 4
ER -