TY - GEN
T1 - A reinforcement learning approach for the report scheduling process under multiple constraints
AU - Méndez-Hernández, Beatriz M.
AU - Coto Palacio, Jessica
AU - Martínez Jiménez, Yailen
AU - Nowé, Ann
AU - Rodríguez Bazan, Erick D.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Scheduling problems appear on a regular basis in many real life situations, whenever it is necessary to allocate resources to perform tasks, optimizing one or more objective functions. Depending on the problem being solved, these tasks can take different forms, and the objectives can also vary. This research addresses scheduling in manufacturing environments, where the reports requested by the customers have to be scheduled in a set of machines with capacity constraints. Additionally, there is a set of limitations imposed by the company that must be taken into account when a feasible solution is built. To solve this problem, a general algorithm is proposed, which initially distributes the total capacity of the system among the existing resources, taking into account the capacity of each them, after that, each resource decides in which order it will process the reports assigned to it. The experimental study performed shows that the proposed approach allows to obtain feasible solutions for the report scheduling problem, improving the results obtained by other scheduling methods.
AB - Scheduling problems appear on a regular basis in many real life situations, whenever it is necessary to allocate resources to perform tasks, optimizing one or more objective functions. Depending on the problem being solved, these tasks can take different forms, and the objectives can also vary. This research addresses scheduling in manufacturing environments, where the reports requested by the customers have to be scheduled in a set of machines with capacity constraints. Additionally, there is a set of limitations imposed by the company that must be taken into account when a feasible solution is built. To solve this problem, a general algorithm is proposed, which initially distributes the total capacity of the system among the existing resources, taking into account the capacity of each them, after that, each resource decides in which order it will process the reports assigned to it. The experimental study performed shows that the proposed approach allows to obtain feasible solutions for the report scheduling problem, improving the results obtained by other scheduling methods.
KW - Dispatching rules
KW - Parallel machines
KW - Reinforcement learning
KW - Reports scheduling
UR - http://www.scopus.com/inward/record.url?scp=85057236660&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01132-1_26
DO - 10.1007/978-3-030-01132-1_26
M3 - Conference paper
AN - SCOPUS:85057236660
SN - 9783030011314
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 235
BT - 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018
A2 - Heredia, Yanio Hernández
A2 - Núñez, Vladimir Milián
A2 - Shulcloper, José Ruiz
PB - Springer Verlag
T2 - 6th International Workshop on Artificial Intelligence and Pattern Recognition
Y2 - 12 September 2018 through 26 September 2018
ER -