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A Multi-objective Optimization Method for Product Feature Fatigue Problem

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Product feature fatigue is a common problem in practice. At the moment of purchasing, customers prefer to choose products with more features. After having used these high-feature products, customers become frustrated or dissatisfied with the usability problems caused by too many features. To deal with product feature fatigue problem, this paper introduces a novel model in which capability and complexity are regarded as two conflicting objects, and NSGA-II is adopted to search for a set of Pareto solutions for this multi-objective optimization problem. Then, this paper establishes piecewise linear membership functions based on decision maker’s preferences, and a priority list of non-dominated solutions can be provided according to the membership function values. The list can make it easier for decision makers to make final selection. A smart phone case study shows that the proposed method is a powerful decision-aid tool for product designers when dealing with feature fatigue problem.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jinze Chai, Ming Li, Yu Zheng, Liya Wang & Fan Yu

Authors
  1. Jinze Chai
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  2. Ming Li
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  3. Yu Zheng
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  4. Liya Wang
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  5. Fan Yu
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Chai, J., Li, M., Zheng, Y., Wang, L., Yu, F. (2014). A Multi-objective Optimization Method for Product Feature Fatigue Problem. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_45

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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