Many-Objective Evolutionary Optimisation and Visual Analytics for Product Family Design

  • Ruchit A. Shah
  • Patrick M. Reed
  • Timothy W. Simpson


Product family design involves the development of multiple products that share common components, modules and subsystems, yet target different market segments and groups of customers. The key to a successful product family is the product platformthe common components, modules and subsystemsaround which the family is derived. The fundamental challenge when designing a family of products is resolving the inherent trade-off between commonality and performance. If there is too much commonality, then individual products may not meet their performance targets; however, too little sharing restricts the economies of scale that can be achieved during manufacturing and production. Multi-objective evolutionary optimisation algorithms have been used extensively to address this trade-off and determine which variables should be common (i.e., part of the platform) and which should be unique in a product family. In this chapter, we present a novel approach based on many-objective evolutionary optimisation and visual analytics to resolve trade-offs between commonality and many performance objectives. We provide a detailed example involving a family of aircraft that demonstrates the challenges of solving a 10-objective trade-off between commonality and the nine performance objectives in the family. Future research directions involving the use of multi-objective optimisation and visual analytics for product family design are also discussed.


Objective Space Product Family Product Platform Product Family Design Good Compromise Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The first and second authors were partially supported by the National Science Foundation (NSF) under CAREER Grant No. CBET-0640443, and the third author acknowledges support from NSF Grant No. CMMI-0620948. The computational experiments in this work were supported in part through instrumentation funded by NSF Grant No. OCI-0821527. Any opinions, findings and conclusions or recommendations in this chapter are those of the authors and do not necessarily reflect the views of the National Science Foundation.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Ruchit A. Shah
    • 1
  • Patrick M. Reed
    • 2
  • Timothy W. Simpson
    • 1
  1. 1.Industrial & Manufacturing EngineeringPennsylvania State UniversityUniversity ParkUSA
  2. 2.Civil & Environmental EngineeringPennsylvania State UniversityUniversity ParkUSA

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