A decomposed genetic algorithm for solving the joint product family optimization problem

Aida Khajavirad, Jeremy J. Michalek, Timothy W. Simpson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

A critical step when designing a successful product family is to determine a cost-saving platform configuration along with an optimally distinct set of product variants that target different market segments. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. However, the high dimensionality of an "all-in-one" algorithm for optimizing the joint problem of 1) platform variable selection, 2) platform design and 3) variant design makes most of these approaches impractical when a large number of products is considered. Many existing approaches have restricted the scope of the problem by fixing platform configuration a priori, limiting platform configuration to an all-or-none component sharing strategy, or by solving subsets of the joint problem in stages, sacrificing optimality. In this study, we propose a single-stage optimization approach for solving the joint product family problem with generalized commonality using an efficient decomposition solution strategy involving multi-objective genetic algorithms (MOGAs). The proposed approach overcomes prior limitations by introducing a generalized two-dimensional commonality chromosome and decomposing the joint formulation into a two-level GA, where the upper-level determines the optimal platform configuration while each lower-level designs one of the individual variants in the family. Moreover, all sub-problems run in parallel, and the upper-level GA coordinates consistency among the lower-levels using the MPI (Message Passing Interface) library. The proposed approach is demonstrated by optimizing a family of three general aviation aircraft, and results outperform those from a non-decomposed GA. Results also show that the commonality-performance Pareto front contains solutions with generalized commonality, suggesting the need to avoid all-or-none component sharing restrictions in order to avoid sub-optimality. Future work in scaling the decomposed CA to larger product families is also discussed.

Original languageEnglish (US)
Title of host publicationCollection of Technical Papers - 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
Pages2111-2124
Number of pages14
ISBN (Print)1563478927, 9781563478925
DOIs
StatePublished - 2007
Event48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference - Waikiki, HI, United States
Duration: Apr 23 2007Apr 26 2007

Publication series

NameCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Volume2
ISSN (Print)0273-4508

Other

Other48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
CountryUnited States
CityWaikiki, HI
Period4/23/074/26/07

All Science Journal Classification (ASJC) codes

  • Architecture
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'A decomposed genetic algorithm for solving the joint product family optimization problem'. Together they form a unique fingerprint.

Cite this