A Prediction-Based Approach for Online Dynamic Appointment Scheduling: A Case Study in Radiotherapy Treatment

Tu San Pham (), Antoine Legrain (), Patrick De Causmaecker () and Louis-Martin Rousseau ()
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Tu San Pham: Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; CIRRELT, Montréal, Québec H3T 1J4, Canada
Antoine Legrain: Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; CIRRELT, Montréal, Québec H3T 1J4, Canada; GERAD, Montréal, Québec H3T 2A7, Canada
Patrick De Causmaecker: KU Leuven, 8500 Kortrijk, Belgium
Louis-Martin Rousseau: Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; CIRRELT, Montréal, Québec H3T 1J4, Canada

INFORMS Journal on Computing, 2023, vol. 35, issue 4, 844-868

Abstract: Patient scheduling is a difficult task involving stochastic factors, such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High-priority patients may arrive at any time, and there must be resources available to accommodate them. A common solution is to reserve a flat percentage of treatment capacity for emergency patients. However, this solution can result in overdue treatments for urgent patients, a failure to fully exploit treatment capacity, and delayed treatments for low-priority patients. This problem is especially severe in large and crowded hospitals. In this paper, we propose a prediction-based approach for online dynamic radiotherapy scheduling that dynamically adapts the present scheduling decision based on each incoming patient and the current allocation of resources. Our approach is based on a regression model trained to recognize the links between patients’ arrival patterns and their ideal waiting time in optimal off-line solutions when all future arrivals are known in advance. When our prediction-based approach is compared with flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients and also maintains comparable waiting times for the other patients. We also demonstrate how our proposed approach supports explainability and interpretability in scheduling decisions using Shapley additive explanation values.

Keywords: operations research; radiotherapy scheduling; uncertainty; patient scheduling; explainability (search for similar items in EconPapers)
Date: 2023
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