Optimization of Customer Satisfaction Using an Improved Simulation Annealing

  • Kit Yan Chan
  • C. K. Kwong
  • Tharam S. Dillon
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 403)


Chapters 3 and 4 discussed using the fuzzy AHP approaches to determine the importance weights of customer requirements of new product designs. Chapters 5 to 8 discussed using fuzzy and evolutionary methods to generate models which represent relationships between customer requirements and the design attributes of new products. Based on the models and the importance weights for customer requirements, the optimization problems for maximizing overall customer satisfaction for the new products can be formulated. However, nonlinearity exists between customer requirements and design attributes of new products. Therefore, these optimization problems have multiple optima arising from local optima, and cannot be handled by classical optimization methods such as gradient-based methods. This chapter discusses a computational intelligence optimization method, namely simulated annealing (SA), to solve these multi-optima problems for new product design.


Simulated Annealing Product Design Customer Satisfaction Candidate Solution Orthogonal Array 
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 Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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