Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence

Tiago Paulinelli Ferreira; Marley Maria Bernardes Rebuzzi Vellasco; Luciana Faletti Almeida; Juan Guillermo Lazo Lazo

doi:10.1109/ISCMI59957.2023.10458546

Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence

Tiago Paulinelli Ferreira
, Marley Maria Bernardes Rebuzzi Vellasco
, Luciana Faletti Almeida
, Juan Guillermo Lazo Lazo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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

Original language	English
Title of host publication	Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023)
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	34-41
Number of pages	8
ISBN (Electronic)	979-8-3503-5936-7
DOIs	https://doi.org/10.1109/ISCMI59957.2023.10458546
State	Published - 2023
Event	2023 10th International Conference on Soft Computing & Machine Intelligence - Mexico City, Mexico Duration: 25 Nov 2023 → 26 Nov 2023 Conference number: 10

Conference

Conference	2023 10th International Conference on Soft Computing & Machine Intelligence
Abbreviated title	ISCMI 2023
Country/Territory	Mexico
City	Mexico City
Period	25/11/23 → 26/11/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 17 Partnerships for the Goals

Keywords

Evolutionary algorithms
Evolutionary computing
Particle swarm optimization
Naval assets
Maintenance and operation plan
Maintenance and Operation Plan

Access to Document

10.1109/ISCMI59957.2023.10458546

Cite this

Paulinelli Ferreira, T., Vellasco, M. M. B. R., Almeida, L. F., & Lazo Lazo, J. G. (2023). Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence. In Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023) (pp. 34-41). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCMI59957.2023.10458546

Paulinelli Ferreira, Tiago ; Vellasco, Marley Maria Bernardes Rebuzzi ; Almeida, Luciana Faletti et al. / Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence. Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023). Piscataway : Institute of Electrical and Electronics Engineers Inc., 2023. pp. 34-41

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abstract = "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",

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author = "\{Paulinelli Ferreira\}, Tiago and Vellasco, \{Marley Maria Bernardes Rebuzzi\} and Almeida, \{Luciana Faletti\} and \{Lazo Lazo\}, \{Juan Guillermo\}",

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year = "2023",

doi = "10.1109/ISCMI59957.2023.10458546",

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Paulinelli Ferreira, T, Vellasco, MMBR, Almeida, LF & Lazo Lazo, JG 2023, Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence. in Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023). Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 34-41, 2023 10th International Conference on Soft Computing & Machine Intelligence, Mexico City, Mexico, 25/11/23. https://doi.org/10.1109/ISCMI59957.2023.10458546

Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence. / Paulinelli Ferreira, Tiago; Vellasco, Marley Maria Bernardes Rebuzzi; Almeida, Luciana Faletti et al.
Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023). Piscataway: Institute of Electrical and Electronics Engineers Inc., 2023. p. 34-41.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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BT - Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023)

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 25 November 2023 through 26 November 2023

ER -

Paulinelli Ferreira T, Vellasco MMBR, Almeida LF, Lazo Lazo JG. Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence. In Proceedings of 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023). Piscataway: Institute of Electrical and Electronics Engineers Inc. 2023. p. 34-41 doi: 10.1109/ISCMI59957.2023.10458546

Optimization of a naval asset maintenance plan through hybrid evolutionary algorithms and swarm intelligence

Abstract

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Otimização de um plano de manutenção de navios por meio de algoritmos evolutivos e inteligência de enxame

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