TY - GEN
T1 - Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence
AU - Paulinelli Ferreira, Tiago
AU - Vellasco, Marley Maria Bernardes Rebuzzi
AU - Almeida, Luciana Faletti
AU - Lazo Lazo, Juan Guillermo
N1 - Conference code: 10
PY - 2023
Y1 - 2023
N2 - The majority of naval industry companies use operation and maintenance plans for their equipment, systems and assets. However, because they are not optimized, such naval operation and maintenance plans are not practical when put into execution, either because they do not plan adequate time gaps between maintenance, or because they do not estimate changes in shipbuilding stages and in available infrastructure. This work addresses an optimization problem with a large solution search space for maintenance and operation plans of naval assets of the Brazilian Navy in which evolutionary computing and swarm intelligence are employed to solve it. It involves the construction of two to six warships over a span of more than half a century. The constraints and parameters used were not found in the literature. The results of the evolutionary model and the combination of genetic and swarm operators are novel, and prove that the proposed model yields improved and viable maintenance and operation plans compared to that obtained by previously used techniques, such as Monte Carlo Simulation
AB - The majority of naval industry companies use operation and maintenance plans for their equipment, systems and assets. However, because they are not optimized, such naval operation and maintenance plans are not practical when put into execution, either because they do not plan adequate time gaps between maintenance, or because they do not estimate changes in shipbuilding stages and in available infrastructure. This work addresses an optimization problem with a large solution search space for maintenance and operation plans of naval assets of the Brazilian Navy in which evolutionary computing and swarm intelligence are employed to solve it. It involves the construction of two to six warships over a span of more than half a century. The constraints and parameters used were not found in the literature. The results of the evolutionary model and the combination of genetic and swarm operators are novel, and prove that the proposed model yields improved and viable maintenance and operation plans compared to that obtained by previously used techniques, such as Monte Carlo Simulation
KW - Algoritmos evolutivos
KW - Computación evolutiva
KW - Optimización por enjambre de partículas
KW - Activos navales
KW - Plan de mantenimiento y operación
KW - Evolutionary algorithms
KW - Evolutionary computing
KW - Particle swarm optimization
KW - Naval assets
KW - Maintenance and operation plan
KW - Maintenance and Operation Plan
UR - http://www.scopus.com/inward/record.url?scp=85188445939&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/a9473da6-e010-3be0-a68e-6468ff7939a0/
U2 - 10.1109/ISCMI59957.2023.10458546
DO - 10.1109/ISCMI59957.2023.10458546
M3 - Conference contribution
SP - 34
EP - 41
BT - Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023)
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 2023 10th International Conference on Soft Computing & Machine Intelligence
Y2 - 25 November 2023 through 26 November 2023
ER -