IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v567y2021ics0378437120308542.html
   My bibliography  Save this article

Research on lean supply chain network model based on node removal

Author

Listed:
  • Zhao, Peixin
  • Yin, Shengnan
  • Han, Xue
  • Li, Zhuyue

Abstract

With the development of economic globalization, the competition among enterprises is increasingly intensified. Many industries have innovated and reformed their supply chains to reduce the structural complexity and enhance their competitive advantage. Since the large number and diversity of nodes are the main reasons for the structural complexity, the lean of supply chain network models is realized by node removal in this paper: a node removal cost model is presented innovatively, and then a memetic algorithm is proposed based on the principles of resource finiteness, structural leanness and network robustness. Compared with some other heuristic algorithms, the effectiveness and efficiency of this algorithm are illustrated by some numerical examples. This research will provide a methodological reference for the lean of supply chain structure.

Suggested Citation

  • Zhao, Peixin & Yin, Shengnan & Han, Xue & Li, Zhuyue, 2021. "Research on lean supply chain network model based on node removal," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
  • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120308542
    DOI: 10.1016/j.physa.2020.125556
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120308542
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2020.125556?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kazi Arif-Uz-Zaman & A.M.M. Nazmul Ahsan, 2014. "Lean supply chain performance measurement," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 63(5), pages 588-612, June.
    2. Yang, Zhirou & Liu, Jing, 2018. "A memetic algorithm for determining the nodal attacks with minimum cost on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1041-1053.
    3. Duan, Boping & Liu, Jing & Zhou, Mingxing & Ma, Liangliang, 2016. "A comparative analysis of network robustness against different link attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 144-153.
    4. Bellingeri, Michele & Cassi, Davide & Vincenzi, Simone, 2014. "Efficiency of attack strategies on complex model and real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 174-180.
    5. Steve Bin Zhou & Fiona Xiaoying Ji, 2015. "Impact of Lean Supply Chain Management on Operational Performance: A Study of Small Manufacturing Companies," International Journal of Business Analytics (IJBAN), IGI Global, vol. 2(3), pages 1-19, July.
    6. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    7. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Complex interdependent supply chain networks: Cascading failure and robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 58-69.
    8. Bellingeri, Michele & Cassi, Davide, 2018. "Robustness of weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 47-55.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kayvan Miri Lavassani & Bahar Movahedi, 2021. "Firm-Level Analysis of Global Supply Chain Network: Role of Centrality on Firm’s Performance," International Journal of Global Business and Competitiveness, Springer, vol. 16(2), pages 86-103, December.

    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. P.B., Divya & Lekha, Divya Sindhu & Johnson, T.P. & Balakrishnan, Kannan, 2022. "Vulnerability of link-weighted complex networks in central attacks and fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    2. Nie, Tingyuan & Fan, Bo & Wang, Zhenhao, 2022. "Complexity and robustness of weighted circuit network of placement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    3. Lekha, Divya Sindhu & Balakrishnan, Kannan, 2020. "Central attacks in complex networks: A revisit with new fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Hao, Yucheng & Jia, Limin & Wang, Yanhui, 2020. "Edge attack strategies in interdependent scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    5. Jisha Mariyam John & Michele Bellingeri & Divya Sindhu Lekha & Davide Cassi & Roberto Alfieri, 2023. "Effect of Weight Thresholding on the Robustness of Real-World Complex Networks to Central Node Attacks," Mathematics, MDPI, vol. 11(16), pages 1-12, August.
    6. Zhang, Haihong & Wu, Wenqing & Zhao, Liming, 2016. "A study of knowledge supernetworks and network robustness in different business incubators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 545-560.
    7. Stefano Martinazzi & Andrea Flori, 2020. "The evolving topology of the Lightning Network: Centralization, efficiency, robustness, synchronization, and anonymity," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    8. Wang, Jianwei & Wang, Siyuan & Wang, Ziwei, 2022. "Robustness of spontaneous cascading dynamics driven by reachable area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    9. Xia Cao & Chuanyun Li & Wei Chen & Jinqiu Li & Chaoran Lin, 2020. "Research on the invulnerability and optimization of the technical cooperation innovation network based on the patent perspective—A case study of new energy vehicles," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    10. Sohn, Insoo, 2019. "A robust complex network generation method based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 593-601.
    11. Bellingeri, M. & Bevacqua, D. & Scotognella, F. & LU, Zhe-Ming & Cassi, D., 2018. "Efficacy of local attack strategies on the Beijing road complex weighted network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 316-328.
    12. Xia, Ling-Ling & Song, Yu-Rong & Li, Chan-Chan & Jiang, Guo-Ping, 2018. "Improved targeted immunization strategies based on two rounds of selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 540-547.
    13. Moore, Jack Murdoch & Small, Michael & Yan, Gang, 2021. "Inclusivity enhances robustness and efficiency of social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    14. Wang, Yingcong & Xiao, Renbin, 2016. "An ant colony based resilience approach to cascading failures in cluster supply network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 150-166.
    15. Yang, Shulan & Hou, Zhiwei & Chen, Hongbo, 2023. "Evaluation of vulnerability of MAV/UAV collaborative combat network based on complex network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    16. Jiang, Yuan & Yan, Yuwei & Hong, Cheng & Yang, Songqing & Yu, Rongbin & Dai, Jiyang, 2022. "Multidirectional recovery strategy against failure," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    17. Aymeric Vié & Alfredo J. Morales, 2021. "How Connected is Too Connected? Impact of Network Topology on Systemic Risk and Collapse of Complex Economic Systems," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1327-1351, April.
    18. Yang, Yu & He, Ze & Song, Zouying & Fu, Xin & Wang, Jianwei, 2018. "Investigation on structural and spatial characteristics of taxi trip trajectory network in Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 755-766.
    19. Yu, Yang & Deng, Ye & Tan, Suo-Yi & Wu, Jun, 2018. "Efficient disintegration strategy in directed networks based on tabu search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 435-442.
    20. Gao, Yan-Li & Chen, Shi-Ming & Nie, Sen & Ma, Fei & Guan, Jun-Jie, 2018. "Robustness analysis of interdependent networks under multiple-attacking strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 495-504.

    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:eee:phsmap:v:567:y:2021:i:c:s0378437120308542. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.