Abstract
In Europe, around half of all patients diagnosed with cancer are treated with radiation therapy. To reduce waiting times, optimizing the use of linear accelerators for treatment is crucial. This paper introduces an Integer Programming (IP) and two Constraint Programming (CP) models for the non-block radiotherapy patient scheduling problem. Patients are scheduled considering priority, pattern, duration, and start day of their treatment. The models include expected future patient arrivals. Treatment time of the day is included in the models as time windows which enable more realistic objectives and constraints. The models are thoroughly evaluated for multiple different scenarios, altering: planning day, machine availability, arrival rates, patient backlog, and the number of time windows in a day. The results demonstrate that the CP models find feasible solutions earlier, while the IP model reaches optimality considerably faster.
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Acknowledgments
The authors thank Per Enqvist at KTH and Kjell Eriksson at RaySearch Laboratories for a fruitful collaboration. The authors are grateful for insightful discussions about the CP models with Mats Carlsson and Peter J. Stuckey and constructive comments from the anonymous reviewers.
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Frimodig, S., Schulte, C. (2019). Models for Radiation Therapy Patient Scheduling. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_25
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DOI: https://doi.org/10.1007/978-3-030-30048-7_25
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