Product Portfolio Selection of Designs Through an Analysis of Lower-Dimensional Manifolds and Identification of Common Properties

  • Madan Mohan Dabbeeru
  • Kalyanmoy Deb
  • Amitabha Mukerjee


Functional commonalities across product families have been considered by a large body of product family design community but this concept is not widely used in design. For a designer, a functional family refers to a set of designs evaluated based on the same set of qualities; the embodiments and the design spaces may differ, but the semantics of what is being measured (e.g., strength of a spring) remain the same. Based on this functional behaviour we introduce a product family hierarchy, where the designs can be classified into phenomenological design family, functional part family and embodiment part family. And then, we consider the set of possible performances of interest to the user at the embodiment level, and use multi-objective optimisation to identify the non-dominated solutions or the Pareto-front. The designs lying along this front are mapped to the design space, which is usually far higher in dimensionality, and then clustered in an unsupervised manner to obtain candidate product groupings which the designer may inspect to arrive at portfolio decisions. We highlight and discuss two recently suggested techniques for this purpose. First, with help of dimensionality reduction techniques, we show how these clusters in low-dimensional manifolds embedded in the high-dimensional design space. We demonstrate this process on three different designs (water faucets, compression springs and electric motors), involving both continuous and discrete design variables. Second, with the help of a data analysis of Pareto-optimal solutions, we decipher common design principles that constitute the product portfolio solutions. We demonstrate this so-called ‘innovization’ principles on a spring design problem. The use of multi-objective optimisation (evolutionary and otherwise) is the key feature of both approaches. The approaches are promising and further research should pave their ways to better design and manufacturing activities.


Design Variable Design Space Product Family Product Platform Locally Linear Embedding 
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.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Madan Mohan Dabbeeru
    • 1
  • Kalyanmoy Deb
    • 1
  • Amitabha Mukerjee
    • 2
  1. 1.Kanpur Genetic Algorithms Laboratory (KanGAL)Indian Institute of Technology KanpurKanpurIndia
  2. 2.Computer Science and Engineering DepartmentIndian Institute of Technology KanpurKanpurIndia

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