Designing Optimal Products: Algorithms and Systems

  • Stelios Tsafarakis
  • Nikolaos Matsatsinis
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 258)


The high cost of a product failure makes it imperative for a company to assess the market penetration of a new product at its early design. In this context, the Optimal Product Line Design problem was formulated thirty five years ago, and remains a significant research topic in the area of quantitative marketing until today. In this chapter we provide a brief description of the problem, which belongs to the class of NP-hard problems, and review the optimization algorithms that have been applied to it. The performance of the algorithms is evaluated, and the best two approaches are applied to simulated data, as well as a real world scenario. Emphasis is placed on Genetic Algorithms, since the results of the study indicate them as the approach that better fits to the specific marketing problem. Finally, the relevant marketing systems that deal with the problem are presented, and their pros and cons are discussed.


Genetic Algorithm Product Line Market Share Optimal Product Attribute Level 
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 Berlin Heidelberg 2010

Authors and Affiliations

  • Stelios Tsafarakis
    • 1
  • Nikolaos Matsatsinis
    • 1
  1. 1.Department of Production and Management Engineering,Decision Support Systems LaboratoryTechnical University of CreteChaniaGreece

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