Skip to main content

Advertisement

SpringerLink
Log in

A multi-objective genetic algorithm approach for solving feature addition problem in feature fatigue analysis

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Feature fatigue (FF) is used to represent the phenomenon of customer’s inconsistent satisfaction with products: customers prefer to choose products with more features and capabilities initially, but after having worked with a product, they become frustrated or dissatisfied with the usability problems caused by too many features. To “defeat” FF, it is essential for designers to decide what features should be added when developing a product to make the product attractive enough and not too hard to use at the same time. In this paper, a feature fatigue multi-objective genetic algorithm (FFMOGA) method is reported for solving the feature addition problem. In the proposed method, fitness functions are established based on Bayesian networks, which can represent the uncertain customer preferences and reflect the relationships among features. The computational experiments on a smart phone case show that the FFMOGA approach can find multiple solutions along the Pareto-optimal frontier for designers to select from, and these obtained solutions have good performance in convergence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bertini M., Ofek E., Ariely D. (2009) The impact of add-on features on consumer product evaluations. Journal of Consumer Research 36: 17–28

    Article  Google Scholar 

  • Chen C., Wang L. (2008) Integrating rough set clustering and grey model to analyse dynamic customer requirements. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222(2): 319–332

    Article  Google Scholar 

  • Chen Y., Carrillo J. E., Vakharia A. J., Sin P. (2010) Fusion product planning: A market offering perspective. Decision Sciences 41(2): 325–353

    Article  Google Scholar 

  • Dash, D., & Druzdzel, M. (2003). Robust independence testing for constraint-based learning of causal structure. In Proceedings of the 19th annual conference on uncertainty in artificial intelligence (UAI-03) (pp. 167–174), San Francisco.

  • Deb K. (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    Google Scholar 

  • Deb K., Pratap A., Agarwal S., Meyarivan T. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2): 182–197

    Article  Google Scholar 

  • Dempster A. P., Laird N. M., Rubin D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B 39(1): 1–38

    Google Scholar 

  • Dumas J. S., Redish J. C. (1993) A practical guide to usability testing. Ablex Publishing Corporation, Norwood

    Google Scholar 

  • Ferber S., Haag J., Savolainen J. (2002) Feature interaction and dependencies: Modeling features for reengineering a legacy product line. In: Chastek G. J. (Ed.), Software product lines. Springer, Heidelberg, pp 235–256

    Chapter  Google Scholar 

  • Gill T. (2008) Convergent products: What functionalities add more value to the base?. Journal of Marketing 72(2): 46–62

    Article  Google Scholar 

  • Gill T., Lei J. (2009) Convergence in the high-technology consumer markets: Not all brands gain equally from adding new functionalities to products. Marketing Letters 20(1): 91–103

    Article  Google Scholar 

  • Guan X. S., Wang Y. Q., Tao L. Y. (2009) Machining scheme selection of digital manufacturing based on genetic algorithm and AHP. Journal of Intelligent Manufacturing 20(6): 661–669

    Article  Google Scholar 

  • Hamilton R. W., Thompson D. V. (2007) Is there a substitute for direct experience? Comparing consumers’ preferences after direct and indirect product experiences. Journal of Consumer Research 34(4): 546–555

    Article  Google Scholar 

  • Heckerman D. (1997) Bayesian networks for data mining. Data mining and knowledge discovery 1(1): 79–119

    Article  Google Scholar 

  • Hiremath, N. C., Sahu, S., & Tiwari, M. K. (2012). Multi objective outbound logistics network design for a manufacturing supply chain. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0635-8 (published online).

  • Hou T. H., Su C. H., Chang H. Z. (2008) An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits. Journal of Intelligent Manufacturing 19(3): 361–374

    Article  Google Scholar 

  • Jiao J., Chen C.-H. (2006) Customer requirement management in product development: A review of research issues. Concurrent Engineering: Research and Applications 14(3): 173–185

    Article  Google Scholar 

  • Kang, K. C., Cohen, S. G., Hess, J. A., Novak, W. E., & Peterson, A. S. (1990). Feature-oriented domain analysis (FODA) feasibility study. Technical Report CMU/SEI-90-TR-021, Software Engineering Institute, Carnegie Mellon University, Pittsburgh.

  • Keijzers, J., den Ouden, E., & Lu, Y. (2008). Usability benchmark study of commercially available smart phones: Cell phone type platform, PDA type platform and PC type platform. In Proceedings of the 10th international conference on human computer interaction with mobile devices and services (pp. 265–272), New York.

  • Klockar, T., Carr D. A., Hedman, A., Johansson, T., & Bengtsson, F. (2003). Usability of mobile phones. In Proceedings of the 19th international symposium on human factors in telecommucation (pp. 197–204), Berlin.

  • Knowles, J., & Corne, D. (1999). The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimisation. In Proceedings of the 1999 congress on evolutionary computation (pp. 98–105), Piscataway, NJ.

  • Kruger J., Galak J., Burrus J. (2007) When consumers’ self-image motives fail. Journal of Consumer Psychology 17(4): 250–253

    Article  Google Scholar 

  • Li M., Wang L. (2011) Feature fatigue analysis in product development using Bayesian networks. Expert Systems with Applications 38(8): 10631–10637

    Article  Google Scholar 

  • Lu J., Bai C., Zhang G. (2009) Cost-benefit factor analysis in e-services using Bayesian networks. Expert Systems with Applications 36(3P1): 4617–4625

    Article  Google Scholar 

  • Murphy G. L., Ross B. H. (1999) Induction with cross-classified categories. Memory and Cognition 27(6): 1024–1041

    Article  Google Scholar 

  • Nowlis S. M., Simonson I. (1996) The effect of new product features on brand choice. Journal of Marketing Research 33(1): 36–46

    Article  Google Scholar 

  • Obendorf H. (2009) Minimalism for interaction design: a proposal. In: Obendorf H. (Ed.), Minimalism: Designing simplicity. Springer, London, pp 65–78

    Chapter  Google Scholar 

  • Pasandideh, S. H. R., Niaki, S. T. A., & Hajipour, V. (2011). A multi-objective facility location model with batch arrivals: Two parameter-tunedmeta-heuristic algorithms. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0592-7 (published online).

  • Rahman M., Rahman M. M. (2009) To defeat feature fatigue the right way, understand it first. Strategic Direction 25(6): 26–28

    Article  Google Scholar 

  • Rajagopal P., Burnkrant R. E. (2009) Consumer evaluations of hybrid products. Journal of Consumer Research 36: 232–241

    Article  Google Scholar 

  • Rust R. T., Thompson D. V., Hamilton R. W. (2006) Defeating feature fatigue. Harvard business review 84(2): 98–107

    Google Scholar 

  • Sääksjärvi M., Samiee S. (2011) Assessing multifunctional innovation adoption via an integrative model. Journal of the Academy of Marketing Science 39(5): 717–735

    Article  Google Scholar 

  • Stock, R. M. (2010). How does product program innovativeness affect customer satisfaction? A comparison of goods and services. Journal of the Academy of Marketing Science. doi:10.1007/s11747-010-0215-4.

  • Thompson D. V., Hamilton R. W., Rust R. T. (2005) Feature fatigue: When product capabilities become too much of a good thing. Journal of Marketing Research 42(4): 431–442

    Article  Google Scholar 

  • Van Veldhuizen, D. V., & Lamont, G. B. (1998). Evolutionary computation and convergence to a Pareto front. In Late breaking papers at the genetic program 1998 conference (pp. 221–228), Stanford.

  • Van Veldhuizen, D. V., & Lamont, G. B. (2000). On measuring multiobjective evolutionary algorithm performance. In Proceedings of CEC2000 (pp. 204–211), New Jersey.

  • Venkatesh R., Mahajan V. (1993) A probabilistic approach to pricing a bundle of products or services. Journal of Marketing Research 30(4): 494–508

    Article  Google Scholar 

  • Wang Y., & Tseng, M. M. (2007). An approach to improve the efficiency of configurators. In Industrial engineering and engineering management, 2007 IEEE international conference (pp. 1332–1336), Singapore.

  • Yang, L., Deuse, J., & Jiang, P. (2012). Multi-objective optimization of facility planning for energy intensive companies. In Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0637-6 (published online).

  • Zheng P., Ni L. M. (2006) Smart phone and next generation mobile computing. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Zhou K., Nakamoto K. (2007) How do enhanced and unique features affect new product preference? The moderating role of product familiarity. Journal of the Academy of Marketing Science 35(1): 53–62

    Article  Google Scholar 

  • Zitzler E., Thiele L. (1999) Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 2(4): 257–272

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, China

    Ming Li, Liya Wang & Mingxing Wu

Authors
  1. Ming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liya Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingxing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liya Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, M., Wang, L. & Wu, M. A multi-objective genetic algorithm approach for solving feature addition problem in feature fatigue analysis. J Intell Manuf 24, 1197–1211 (2013). https://doi.org/10.1007/s10845-012-0652-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0652-7

Keywords

Search

Navigation