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Development of the mitigation strategy against the schedule risks of the R&D project through controlling the cascading failure of the R&D network

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  • Wang, Jingbei
  • Yang, Naiding
  • Zhang, Yanlu
  • Song, Yue

Abstract

For R&D projects, the cascading failure among R&D firms will lead to the schedule risks of the R&D tasks, which may lead to potential severe consequences. It is necessary to develop mitigation strategies against cascading failures, so as to reduce schedule risks of R&D projects. Firstly, we propose the BBV algorithm to build the R&D network. Secondly, we build the model of the cascading failures of the R&D network based on the CA model. Thirdly, we develop the mitigation strategies against the schedule risks of the R&D project through controlling the cascading failure. Finally, we analyze different effectiveness of these mitigation strategies against the cascading failures of the R&D network under different values of some critical parameters and different attack strategies. The simulation results show that with the increase of μ and β, the schedule risk of the task network gradually decreases. With the increase of the control parameters ζ, the schedule risk of the task network gradually increases. In any case, the effectiveness of global immunization is better than local immunization, when we know the global information, HI is better than KI, and when we only know the local information, IAI is better than AI. The effectiveness of mitigation strategy under random attack strategy is the best, followed by high-degree attack strategy and high-centrality attack strategy. This provides a new useful theoretical basis on how to keep the safety of the schedule of the R&D project proactively against the cascading failure of the R&D firms in the real world.

Suggested Citation

  • Wang, Jingbei & Yang, Naiding & Zhang, Yanlu & Song, Yue, 2018. "Development of the mitigation strategy against the schedule risks of the R&D project through controlling the cascading failure of the R&D network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 390-401.
  • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:390-401
    DOI: 10.1016/j.physa.2018.05.108
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    References listed on IDEAS

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    Cited by:

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    3. Zhang, Yao & Zuo, Fei & Guan, Xin, 2020. "Integrating case-based analysis and fuzzy optimization for selecting project risk response actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
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    6. Li, Ruimeng & Yang, Naiding & Yi, Hao & Jin, Na, 2023. "The robustness of complex product development projects under design change risk propagation with gray attack information," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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