IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i12p4480-d186200.html
   My bibliography  Save this article

Identifying Core Parts in Complex Mechanical Product for Change Management and Sustainable Design

Author

Listed:
  • Na Zhang

    (College of Mechanical Engineering, Chongqing University, No. 174 Shazhengjie, Shapingba, Chongqing 400044, China)

  • Yu Yang

    (College of Mechanical Engineering, Chongqing University, No. 174 Shazhengjie, Shapingba, Chongqing 400044, China)

  • Jianxin Wang

    (College of Mechanical Engineering, Chongqing University, No. 174 Shazhengjie, Shapingba, Chongqing 400044, China)

  • Baodong Li

    (College of Mechanical Engineering, Chongqing University, No. 174 Shazhengjie, Shapingba, Chongqing 400044, China)

  • Jiafu Su

    (Chongqing Key Laboratory of Electronic Commerce & Supply Chain System, Chongqing Technology and Business University, Chongqing 400067, China)

Abstract

Changes in customer needs are unavoidable during the design process of complex mechanical products, and may bring severely negative impacts on product design, such as extra costs and delays. One of the effective ways to prevent and reduce these negative impacts is to evaluate and manage the core parts of the product. Therefore, in this paper, a modified Dempster-Shafer (D-S) evidential approach is proposed for identifying the core parts. Firstly, an undirected weighted network model is constructed to systematically describe the product structure. Secondly, a modified D-S evidential approach is proposed to systematically and scientifically evaluate the core parts, which takes into account the degree of the nodes, the weights of the nodes, the positions of the nodes, and the global information of the network. Finally, the evaluation of the core parts of a wind turbine is carried out to illustrate the effectiveness of the proposed method in the paper. The results show that the modified D-S evidential approach achieves better performance regarding the evaluation of core parts than the node degree centrality measure, node betweenness centrality measure, and node closeness centrality measure.

Suggested Citation

  • Na Zhang & Yu Yang & Jianxin Wang & Baodong Li & Jiafu Su, 2018. "Identifying Core Parts in Complex Mechanical Product for Change Management and Sustainable Design," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4480-:d:186200
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/12/4480/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/12/4480/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tina Wakolbinger & Anna Nagurney, 2004. "Dynamic supernetworks for the integration of social networks and supply chains with electronic commerce: modeling and analysis of buyer–seller relationships with computations," Netnomics, Springer, vol. 6(2), pages 153-185, October.
    2. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    3. Dangalchev, Chavdar, 2006. "Residual closeness in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 556-564.
    4. Jose M. Cruz & Anna Nagurney & Tina Wakolbinger, 2006. "Financial engineering of the integration of global supply chain networks and social networks with risk management," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(7), pages 674-696, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    2. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    3. Hu, Jianqiang & Yu, Jie & Cao, Jinde & Ni, Ming & Yu, Wenjie, 2014. "Topological interactive analysis of power system and its communication module: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 99-111.
    4. Sun, Hong-liang & Chen, Duan-bing & He, Jia-lin & Ch’ng, Eugene, 2019. "A voting approach to uncover multiple influential spreaders on weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 303-312.
    5. Wang, Xiaojie & Su, Yanyuan & Zhao, Chengli & Yi, Dongyun, 2016. "Effective identification of multiple influential spreaders by DegreePunishment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 238-247.
    6. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    7. Zugang Liu & Anna Nagurney, 2009. "An integrated electric power supply chain and fuel market network framework: Theoretical modeling with empirical analysis for New England," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(7), pages 600-624, October.
    8. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    9. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    10. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Yi, Dongyun, 2018. "Effectively identifying multiple influential spreaders in term of the backward–forward propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 404-413.
    11. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    12. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    13. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    14. Belik, Ivan & Hexmoor, Henry, 2013. "The Multifactor Model of the Agent’s Power in Social Networks," Discussion Papers 2013/11, Norwegian School of Economics, Department of Business and Management Science.
    15. Chen, Yahong & Li, Jinlin & Huang, He & Ran, Lun & Hu, Yusheng, 2017. "Encouraging information sharing to boost the name-your-own-price auction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 108-117.
    16. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    17. Zhou, Ming-Yang & Xiong, Wen-Man & Wu, Xiang-Yang & Zhang, Yu-Xia & Liao, Hao, 2018. "Overlapping influence inspires the selection of multiple spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 76-83.
    18. David Wittstruck & Frank Teuteberg, 2012. "Understanding the Success Factors of Sustainable Supply Chain Management: Empirical Evidence from the Electrics and Electronics Industry," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 19(3), pages 141-158, May.
    19. Zhe Li & Xinyu Huang, 2023. "Identifying Influential Spreaders Using Local Information," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    20. Toyasaki, Fuminori & Daniele, Patrizia & Wakolbinger, Tina, 2014. "A variational inequality formulation of equilibrium models for end-of-life products with nonlinear constraints," European Journal of Operational Research, Elsevier, vol. 236(1), pages 340-350.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4480-:d:186200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.