Stochastic radiotherapy appointment scheduling

Abstract

When scheduling the starting times for treatment appointments of patients in hospitals or outpatient clinics such as radiotherapy centers, minimizing patient waiting time and simultaneously maximizing resource usage is crucial. Significant uncertainty in the treatment durations makes scheduling those activities particularly challenging. In addition to the treatments themselves, also preparation times and exiting times have to be considered, which are uncertain as well. To address and analyze this type of problems, the current study develops a model for planning appointment times under uncertain activity durations for a medical unit with a single “core resource” (in our application case a radiotherapy beam device), several treatment rooms, and required preparation and exiting phases for each patient. We employ a novel buffer concept based on quantiles of duration distributions and introduce a reactive procedure that adapts a pre-determined baseline schedule to the actual patient flow. For heuristically solving the resulting stochastic optimization model, a combination of a Genetic Algorithm and Monte Carlo simulation is proposed. A case study uses real-world data on activity durations gathered from an ion beam therapy facility in Austria. Experimental results comparing different variants of the method are carried out. In particular, comparisons of the stochastic optimization approach to a simpler deterministic approach are given.

A Column Generation Approach for Radiation Therapy Patient Scheduling with Planned Machine Unavailability and Uncertain Future Arrivals

“…Using different heuristic methods, Vogl et al [26] and Maschler et al [27] both create a schedule with treatments close to a pre-defined target time. Accounting for uncertain activity durations, Braune et al [28] present a stochastic optimization model for appointment scheduling in PT and solve it using heuristics.…”

A Review of Optimization Studies for System Appointment Scheduling

“…Various algorithms are applied to the optimization of AS systems, with genetic algorithms (GAs) being the most utilized. For instance, Braune et al [112] proposed a combination of GAs and Monte Carlo simulation to heuristically solve a stochastic optimization model developed for planning the appointment times of healthcare units under uncertain activity durations, allowing to minimize the waiting time of patients while maximizing the use of resources. Fan et al [113] considered patient preferences for highly qualified general practitioners and specialist doctors.…”

Central European journal of operations research (CJOR) “operations research applied to health services (ORAHS) in Europe: general trends and ORAHS 2020 conference in Vienna, Austria”

“…A dedicated session on chemotherapy and radiotherapy scheduling at the ORAHS 2020 included presentations of Robbes et al (2020) on the production of chemotherapies, of Duenas et al (2020) on multicriteria decision analysis modelling in an outpatient chemotherapy service, and of Aringhieri et al (2020a,b) on online algorithms for the radiotherapy patient scheduling problem. A recent paper by Braune et al (2021) presents a model for planning appointment times for radiotherapy treatments with uncertain activity durations, several treatment rooms, and required preparation as well as exiting phases for each patient.…”