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Genetic Algorithm for Optimization of the Replacement Schedules for Major Surface Combatants

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Theory and Practice of Natural Computing (TPNC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11934))

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Abstract

The replacement of aging naval fleets is a multi-faceted problem with many important factors and scheduling constraints, where the development of a sound transition plan relies on leveraging flexibilities. A successful fleet schedule should aim to balance operational requirements, fleet maintenance facility workloads and the availability of ready personnel (operators and maintainers) while conforming to shipbuilding schedules and service life limitations. To evaluate different transition strategies, optimized fleet schedules are generated using a Genetic algorithm with limited-range integer codons. This approach is illustrated with the analysis of five transition scenarios for the replacement of a nine ship frigate class.

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Author information

Authors and Affiliations

  1. Maritime Operation Research Team, Defence Research and Development Canada, Ottawa, Canada

    Michele Fee, Jean-Denis Caron & Van Fong

  2. Bureau of Life Cycle Analysis, Defence Material Organization, Utrecht, Netherlands

    Bart van Oers & Richard Logtmeijer

Authors
  1. Michele Fee
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  2. Bart van Oers
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  3. Richard Logtmeijer
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  4. Jean-Denis Caron
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  5. Van Fong
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Corresponding author

Correspondence to Michele Fee .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  2. Royal Military College of Canada, Kingston, ON, Canada

    Geoffrey Pond

  3. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

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Fee, M., van Oers, B., Logtmeijer, R., Caron, JD., Fong, V. (2019). Genetic Algorithm for Optimization of the Replacement Schedules for Major Surface Combatants. In: Martín-Vide, C., Pond, G., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2019. Lecture Notes in Computer Science(), vol 11934. Springer, Cham. https://doi.org/10.1007/978-3-030-34500-6_11

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  • DOI: https://doi.org/10.1007/978-3-030-34500-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34499-3

  • Online ISBN: 978-3-030-34500-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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