Optimal Commonality Decisions in Multiple Ship Classes

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.

[1]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[2]  Michael J. Corl.  Methodology for Optimizing Commonality Decisions in Multiple Classes of Ships. , 2007 .

[3]  Andrzej Osyczka,et al.  Evolutionary Algorithms for Single and Multicriteria Design Optimization , 2001 .

[4]  Kikuo Fujita,et al.  Product Variety Optimization Simultaneously Designing Module Combination and Module Attributes , 2004, Concurr. Eng. Res. Appl..

[5]  Bilal M. Ayyub,et al.  Elicitation of expert opinions for uncertainty and risks: Answer to the Book Review by Roger M. Cooke , 2003, Fuzzy Sets Syst..

[6]  Michael P. Martinez,et al.  Effective Product Family Design Using Physical Programming , 2002 .

[7]  Michael G. Parsons,et al.  Methodology for the optimization of commonality in multiple ship classes , 2007 .

[8]  J Corl Michael,et al.  Platform-Based Design of a Family of Ships Considering Both Performance and Savings , 2007 .

[9]  Zahed Siddique,et al.  Product platform and product family design : methods and applications , 2010 .

[10]  Timothy W. Simpson,et al.  Product platform design and customization: Status and promise , 2004, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[11]  Marco Laumanns,et al.  PISA: A Platform and Programming Language Independent Interface for Search Algorithms , 2003, EMO.

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Timothy W. Simpson,et al.  A Variation-Based Method for Product Family Design , 2002 .

[14]  Panos Y. Papalambros,et al.  Quantitative platform selection in optimal design of product families, with application to automotive engine design , 2006 .

[15]  A. M. D'Arcangelo,et al.  Ship design and construction , 1969 .

[16]  Steven Francis Zalek.  Multicriterion evolutionary optimization of ship hull forms for propulsion and seakeeping. , 2007 .

[17]  Farrokh Mistree,et al.  Product platform design: method and application , 2001 .

[18]  Javier P. Gonzalez-Zugasti,et al.  A Method for Architecting Product Platforms , 2000 .

[19]  Panos Y. Papalambros,et al.  Platform Selection Under Performance Bounds in Optimal Design of Product Families , 2005 .

[20]  Wei Chen,et al.  A Variation-Based Methodology for Product Family Design , 2000, Volume 2: 26th Design Automation Conference.

[21]  Panos Y. Papalambros,et al.  Platform Selection Under Performance Loss Constraints in Optimal Design of Product Families , 2002, DAC 2002.

[22]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

[23]  Alan Brown,et al.  Multiple‐Objective Optimization in Naval Ship Design , 2003 .

[24]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.