Models, algorithms and performance analysis for adaptive operating room scheduling

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 theoretica

Scheduling routine and call-in clinical appointments with revisits

This paper studies the problem of clinical appointment scheduling when taking revisits into account. We consider two classes of patients: (1) routine patients who have made an appointment weeks in advance and (2) same-day patients who call in at the very beginning of the day, before the first clinical consultation begins. After the first appointment and consultation, patients might need an additional examination and a second consultation to confirm their health status. This paper aims to create an advanced scheduling method for both routine patients and same-day patients to optimise the expected weighted sum of three performance measures: patients’ waiting time, physician’s idle time and overtime. A stochastic programme model is constructed and solved by sample average approximation and benders’ decomposition. Numerical tests show that revisits significantly affect the three performance measures; to improve the hospital system’s operation management, both scheduling of appointment times and daily workload plans are taken into account

Models, algorithms and performance analysis for adaptive operating room scheduling

\u3cp\u3eThe 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.\u3c/p\u3