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Optimization Design Method of PSS Based on GA and QFD

Received: 19 October 2016     Accepted: 5 November 2016     Published: 8 December 2016
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Abstract

Due to sustainable production and consumption, customer-centric business model, increasingly homogenization of products and other factors, manufacturers had to adjust existing design and production mode, making their focus gradually shifted from a single product design and production model to a integration system solution which contains product and serviceto meet the customers’ need. Product Service Systemis a new ideal proposed to adapt this strategy shift of Manufacturer Company. In this paper, proposing the optimization design method of Product service system based on Quality Function Deployment (QFD) and Genetic Algorithms (GA) to solve the problem as normative in design process, unity in design result and other problems.

Published in Science Research (Volume 4, Issue 5)
DOI 10.11648/j.sr.20160405.15
Page(s) 133-140
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Product Service System (PSS), Optimization Design Method, Quality Function Deployment (QFD), Genetic Algorithms (GA)

References
[1] E. Sundin, B. Bras, Making functional sales environmentally and economicallybeneficial through product remanufacturing, J. Clean. Prod. 13 (9) (2005)913–925.
[2] J. Lindstro ¨m, M. Lo fstrand, M. Karlberg, L. Karlsson, A development process forfunctional products: hardware, software, service support system andmanagement of operation, Int. J. Prod. Dev. 16 (3) (2012) 284–303.
[3] Min Qu, Suihuai Yu, Dengkai Chen, Jianjie Chu, BaozhenTian, State-of-the-art of design, evaluation, and operation methodologies inproduct service systems. Computers in Industry 77 (2016) 1–14.
[4] Mont O, Clarifying the concept of product service system [J]. Cleaner Production, 2002, 3(10):237-245.
[5] Mont O, Product-service systems: Panacea or myth?[J]. Doctoral Dissertation, 2004, LundUniversity, Sweden.
[6] Tukker Arnold. Eight types of product-service system: eight ways to sustainability? Experiencefrom suspronet[J]. Business Strategy and the Environment, 2004, 13:246-260.
[7] Sakao T, Lindahl M. Introduction/Service-system Design[M]. [s.l.]: Spring Verlag, 2009.
[8] SUNDIN E, LINDAHL M, IJOMAH W. Product design for product/service systems: Designexperience from Swedish industry[J]. Journal of Manufacturing Technology Management, 2009, 20(5):723-753.
[9] David BL, Andrew K. Case-Based CBR: capturing and reusing reasoning about case adaptation [J].International Journal of Expert Systems, 1997, 10(2):201-215.
[10] Wasserman, X. P., Bode, J., Ren, S. J. Neural networks in quality function deployment. Computers and Industrial Engineering. 1996, 31(3/4): 669-673.
[11] Lu, M. H., Madu, C. N., Kuei, C. and Winokur, D. Integrating QFD, AHP and benchmarkingin strategic marketing. Journal of Business & Industrial Marketing. 1994, 9(1): 41-50.
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Cite This Article
  • APA Style

    Zhang Jianmin, Zhou Jun, Liang Hui. (2016). Optimization Design Method of PSS Based on GA and QFD. Science Research, 4(5), 133-140. https://doi.org/10.11648/j.sr.20160405.15

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    ACS Style

    Zhang Jianmin; Zhou Jun; Liang Hui. Optimization Design Method of PSS Based on GA and QFD. Sci. Res. 2016, 4(5), 133-140. doi: 10.11648/j.sr.20160405.15

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    AMA Style

    Zhang Jianmin, Zhou Jun, Liang Hui. Optimization Design Method of PSS Based on GA and QFD. Sci Res. 2016;4(5):133-140. doi: 10.11648/j.sr.20160405.15

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  • @article{10.11648/j.sr.20160405.15,
      author = {Zhang Jianmin and Zhou Jun and Liang Hui},
      title = {Optimization Design Method of PSS Based on GA and QFD},
      journal = {Science Research},
      volume = {4},
      number = {5},
      pages = {133-140},
      doi = {10.11648/j.sr.20160405.15},
      url = {https://doi.org/10.11648/j.sr.20160405.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20160405.15},
      abstract = {Due to sustainable production and consumption, customer-centric business model, increasingly homogenization of products and other factors, manufacturers had to adjust existing design and production mode, making their focus gradually shifted from a single product design and production model to a integration system solution which contains product and serviceto meet the customers’ need. Product Service Systemis a new ideal proposed to adapt this strategy shift of Manufacturer Company. In this paper, proposing the optimization design method of Product service system based on Quality Function Deployment (QFD) and Genetic Algorithms (GA) to solve the problem as normative in design process, unity in design result and other problems.},
     year = {2016}
    }
    

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    T1  - Optimization Design Method of PSS Based on GA and QFD
    AU  - Zhang Jianmin
    AU  - Zhou Jun
    AU  - Liang Hui
    Y1  - 2016/12/08
    PY  - 2016
    N1  - https://doi.org/10.11648/j.sr.20160405.15
    DO  - 10.11648/j.sr.20160405.15
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    JO  - Science Research
    SP  - 133
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    UR  - https://doi.org/10.11648/j.sr.20160405.15
    AB  - Due to sustainable production and consumption, customer-centric business model, increasingly homogenization of products and other factors, manufacturers had to adjust existing design and production mode, making their focus gradually shifted from a single product design and production model to a integration system solution which contains product and serviceto meet the customers’ need. Product Service Systemis a new ideal proposed to adapt this strategy shift of Manufacturer Company. In this paper, proposing the optimization design method of Product service system based on Quality Function Deployment (QFD) and Genetic Algorithms (GA) to solve the problem as normative in design process, unity in design result and other problems.
    VL  - 4
    IS  - 5
    ER  - 

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Author Information
  • College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai, China

  • College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai, China

  • College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai, China

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