Mixed-Integer Linear programming for scheduling of radiotherapy patients
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Abstract
This study presents an advanced mathematical model to optimize the scheduling of radiotherapy patients, thereby expediting solution discovery. The paper commences with an in-depth analysis of cancer treatment protocols and prior mathematical models. We then introduce some enhancements to an existing mathematical framework with the intent of expediting the derivation of solutions. The validity of the model is ensured through a meticulous evaluation of the constraints, leading to the removal of redundant constraints. This improved model is validated through the generation and assessment of five small-scale cases, and its efficacy is confirmed. The experimental results underscore the substantial time reduction achieved by the enhanced mathematical model in terms of finding solutions. To bolster its applicability to real-world scenarios, the model is enriched by incorporating additional constraints, for example related to surgical and radiotherapy processing times. The application of this comprehensive model to a real-world case demonstrates its ability to accurately determine the durations of simulation and radiotherapy while adhering to the specified constraints. It successfully allocates patients to specific rooms and technologies, and outlines the optimal frequency for radiotherapy sessions within each interval. The proposed model is expected to assume a pivotal role in facilitating informed decision-making among stakeholders. By substantially curtailing the treatment planning time and mitigating errors in radiotherapy patient scheduling, this model will be a valuable asset to healthcare practitioners and decision-makers alike.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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