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Optimal Commonality Decisions in Multiple Ship Classes

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Abstract

A methodology is presented for the determination of the Pareto optimal choice of components and elements to make common between two different classes of military vessels. The use of commonality can produce fleet-wide savings in component purchasing, training, spare parts, vessel construction, etc. The methodology presented here determines the optimal commonality decision and designs the vessel classes to maximize the mission performance per average acquisition cost of each vessel class and the total fleet saving achieved by the commonality. A customized evolutionary algorithm is used to determine the resulting discrete Pareto surface. The methodology is illustrated by its application to the design of two ship classes to perform the specific missions of the US Coast Guard’s National Security Cutter and Offshore Patrol Cutter. The results show that the methodology is effective and that not all commonality choices produce a net savings.

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Acknowledgments

This development of the commonality optimization methodology described here was possible with academic leave and related support of the US Coast Guard for the lead author and support from the Office of Naval Research through N00014-03-0983 for all authors. The authors would like to thank the US Coast Guard for the use of their ship synthesis model and emphasize that this research is by no means intended to be a comparison to any US Coast Guard design work for either the OPC or the NSC. The initial mission requirements for the endurance cutter designs were used for academic purposes only.

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Correspondence to Michael G. Parsons .

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Corl, M.J., Parsons, M.G., Kokkolaras, M. (2014). Optimal Commonality Decisions in Multiple Ship Classes. In: Simpson, T., Jiao, J., Siddique, Z., Hölttä-Otto, K. (eds) Advances in Product Family and Product Platform Design. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7937-6_25

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  • DOI: https://doi.org/10.1007/978-1-4614-7937-6_25

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  • Publisher Name: Springer, New York, NY

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  • Online ISBN: 978-1-4614-7937-6

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