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Service provider portfolio selection for project management using a BP neural network

Author

Listed:
  • Libiao Bai

    (Chang’an University)

  • Kanyin Zheng

    (Chang’an University)

  • Zhiguo Wang

    (Chang’an University)

  • Jiale Liu

    (Chang’an University)

Abstract

Service provider portfolio selection (SPPS) can be a major challenge for organizations to achieve project success. Hence, organizations need to decide on which service provider portfolio (SPP) is appropriate for project management (PM). However, there has been limited research on how to select a SPP in PM. To address this research gap, we establish a novel model for SPPS based on a BP neural network integrated with entropy-AHP from the perspective of the comprehensive economic benefit. This model employs a BP neural network due to its robustness and memory and nonlinear mapping abilities. Furthermore, we implement the proposed model for a construction project to verify the effectiveness. Our results indicate that the model performs well with a prediction accuracy of 97%. Moreover, the model is confirmed to be robust as it still achieves high prediction accuracy when the input data are disturbed randomly.

Suggested Citation

  • Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03878-0
    DOI: 10.1007/s10479-020-03878-0
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    3. Cui, Tianxiang & Ding, Shusheng & Jin, Huan & Zhang, Yongmin, 2023. "Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach," Economic Modelling, Elsevier, vol. 119(C).

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