GeneRa: A problem generator for radiotherapy treatment scheduling problems

Juan-Pablo Cares 1 Maria Cristina Riff 1 Bertrand Neveu 2, 3, 4
3 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : 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.
Type de document :
Article dans une revue
Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2016, 76 (1-2), pp.191-214. 〈10.1007/s10472-015-9477-3〉
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https://hal-enpc.archives-ouvertes.fr/hal-01376581
Contributeur : Bertrand Neveu <>
Soumis le : mercredi 5 octobre 2016 - 11:39:00
Dernière modification le : jeudi 5 juillet 2018 - 14:29:10

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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, Springer Verlag, 2016, 76 (1-2), pp.191-214. 〈10.1007/s10472-015-9477-3〉. 〈hal-01376581〉

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