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Marketing Intelligent Systems Using Soft Computing pp 295–336Cite as

Designing Optimal Products: Algorithms and Systems

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 258))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Production and Management Engineering,Decision Support Systems Laboratory, Technical University of Crete, Chania, Greece

    Stelios Tsafarakis & Nikolaos Matsatsinis

Authors
  1. Stelios Tsafarakis
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  2. Nikolaos Matsatsinis
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Editor information

Editors and Affiliations

  1. University of Granada, Spain

    Jorge Casillas  & Francisco J. Martínez-López  & 

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Tsafarakis, S., Matsatsinis, N. (2010). Designing Optimal Products: Algorithms and Systems. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_19

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  • DOI: https://doi.org/10.1007/978-3-642-15606-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15605-2

  • Online ISBN: 978-3-642-15606-9

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