10:30 AM - 11:00 AM
Stochastic optimization of the scheduling of a radiotherapy center
Abstract. Cancer treatment facilities can improve their eﬃciency for radiation therapy by optimizing the utilization of the linear accelerators (linacs). We propose a method to schedule patients on such machines taking into account their priority for treatment, the maximum waiting time before the ﬁrst treatment, the treatment duration, and the preparation of this treatment (dosimetry). At each arrival of a patient, the future workloads of the linacs and the dosimetry are inferred. We propose a genetic algorithm, which schedules future tasks in dosimetry.This approach ensures the beginning of the treatment on time and thus avoids the cancellation of treatment sessions on linacs. Results indicate the improvements of this new procedure.
11:00 AM - 11:30 AM
Scheduling Appointment Series for Rehabilitation Patients, Taking Future Requests Into Account
We study an online appointment scheduling problem where arriving patients require a series of appointments at multiple departments that may be simultaneous, in series or both, within a certain access time.
This research is motivated by a study of rehabilitation scheduling practices at the Sint Maartenskliniek (the Netherlands). In practice, the prescribed treatments and activities are typically scheduled in the first available time intervals, leaving no room for acute patients who require an appointment series on a short notice. This leads to large access times for acute patients, which has a negative effect on the quality of care and the patients’ satisfaction.
Our objective is to schedule the appointment series of a patient at the moment of his/her arrival, in such a way that the total number of requests scheduled within their required access time is maximized. We formulate this problem as a Markov Decision Process (MDP), that takes into account the current state of already scheduled appointments, and future arrivals. We develop heuristic policies to obtain approximate solutions.
Using simulation, we compare the performance of the MDP to practical and easy-to-use heuristic decision rules.
11:30 AM - 12:00 PM
Proactive On-call Scheduling during a Seasonal Epidemic
Overcrowding in Emergency Departments (EDs) is particularly problematic during seasonal epidemic crises. Each year during this period, EDs set off recourse actions to cope with the increase in workload. Uncertainty in the length and amplitude of epidemics make managerial decisions difficult. We propose in this study a staff allocation model to manage the situation using on-calls. An on-call scheduling policy is proposed to best balance between demand coverage and labor cost under legal constraints of working time. The problem is modelled as a two-stage stochastic Integer Linear Program (ILP) and solved using a Sample Average Approximation (SAA) method. Several epidemic scenarios are defined with data from an ED in Lille, France.