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Article Dans Une Revue Annals of Mathematics and Artificial Intelligence Année : 2016

GeneRa: A problem generator for radiotherapy treatment scheduling problems

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Résumé

Radiotherapy scheduling problems are hard constrained problems that involve many resources including doctors, patients and machines. These problems are different according to the country, and can even vary among institutions in the same country. Due to the lack of standard benchmarks, the algorithms proposed in the literature are very specific and they are not easily comparable or adaptable. We describe the radiotherapy scheduling problem in different countries in order to identify common components. Our goal in this paper is to provide a configurable benchmark generator for this problem that is able to take into account the specific characteristics inherent to each country and institution. The benchmark generator is available online.
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Dates et versions

hal-01376581 , version 1 (05-10-2016)

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Juan-Pablo Cares, Maria Cristina Riff, Bertrand Neveu. GeneRa: A problem generator for radiotherapy treatment scheduling problems. Annals of Mathematics and Artificial Intelligence, 2016, 76 (1-2), pp.191-214. ⟨10.1007/s10472-015-9477-3⟩. ⟨hal-01376581⟩
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