IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v308y2022i1d10.1007_s10479-020-03878-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03878-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03878-0?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. Saaty, Thomas L., 1978. "Modeling unstructured decision problems — the theory of analytical hierarchies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 20(3), pages 147-158.
    2. Sigal Kordova & Eyal Katz & Moti Frank, 2019. "Managing development projects—The partnership between project managers and systems engineers," Systems Engineering, John Wiley & Sons, vol. 22(3), pages 227-242, May.
    3. Jie Yang & Jiafu Su & Lijun Song, 2019. "Selection of Manufacturing Enterprise Innovation Design Project Based on Consumer’s Green Preferences," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    4. Nasuh Buyukkaramikli & Henny Ooijen & J. Bertrand, 2015. "Integrating inventory control and capacity management at a maintenance service provider," Annals of Operations Research, Springer, vol. 231(1), pages 185-206, August.
    5. M.Y. Hu & M.S. Hung & B.E. Patuwo & M.S. Shanker, 1999. "Estimating the performance of Sino‐Hong Kong joint ventures using neuralnetwork ensembles," Annals of Operations Research, Springer, vol. 87(0), pages 213-232, April.
    6. Bharat Jain & Barin Nag, 1998. "A neural network model to predict long-run operating performance of new ventures," Annals of Operations Research, Springer, vol. 78(0), pages 83-110, January.
    7. Nihat Kasap & Hasan Hüseyin Turan & Hüseyin Savran & Berna Tektas-Sivrikaya & Dursun Delen, 2018. "Provider selection and task allocation in telecommunications with QoS degradation policy," Annals of Operations Research, Springer, vol. 263(1), pages 311-337, April.
    8. Xinyi Zhou & Yong Hu & Yong Deng & Felix T. S. Chan & Alessio Ishizaka, 2018. "A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP," Annals of Operations Research, Springer, vol. 271(2), pages 1045-1066, December.
    9. Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
    10. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
    11. Youwen Zhong & Xiaoling Wu, 2020. "Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.
    12. Seongtae Kim & M. Ramkumar & Nachiappan Subramanian, 2019. "Logistics service provider selection for disaster preparation: a socio-technical systems perspective," Annals of Operations Research, Springer, vol. 283(1), pages 1259-1282, December.
    13. Huazan Liu & Yukang He & Qichao Hu & Jianfei Guo & Lan Luo, 2020. "Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
    14. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
    15. Yunqi Zhao & Jing Xiang & Jiaming Xu & Jinying Li & Ning Zhang, 2019. "Study on the Comprehensive Benefit Evaluation of Transnational Power Networking Projects Based on Multi-Project Stakeholder Perspectives," Energies, MDPI, vol. 12(2), pages 1-21, January.
    16. Rajesh Kr. Singh & Angappa Gunasekaran & Pravin Kumar, 2018. "Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach," Annals of Operations Research, Springer, vol. 267(1), pages 531-553, August.
    17. Bhaskar B. Gardas & Rakesh D. Raut & Annasaheb H. Jagtap & Pradeep Yadav, 2019. "Service provider's rationalisation for the performance improvement of the organisation: a case study," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 26(1), pages 21-33.
    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. Hadeel Alharbi & Obaid Alshammari & Houssem Jerbi & Theodore E. Simos & Vasilios N. Katsikis & Spyridon D. Mourtas & Romanos D. Sahas, 2023. "A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
    2. Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    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).

    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. Patanjal Kumar & Sachin Kumar Mangla & Yigit Kazancoglu & Ali Emrouznejad, 2023. "A decision framework for incorporating the coordination and behavioural issues in sustainable supply chains in digital economy," Annals of Operations Research, Springer, vol. 326(2), pages 721-749, July.
    2. Tian, Yuanyuan & Bai, Libiao & Wei, Lan & Zheng, Kanyin & Zhou, Xinyu, 2022. "Modeling for project portfolio benefit prediction via a GA-BP neural network," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    3. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    4. Guh, Yuh-Yuan, 1997. "Introduction to a new weighting method -- Hierarchy consistency analysis," European Journal of Operational Research, Elsevier, vol. 102(1), pages 215-226, October.
    5. Tim Gruchmann & Nadine Pratt & Jan Eiten & Ani Melkonyan, 2020. "4PL Digital Business Models in Sea Freight Logistics: The Case of FreightHub," Logistics, MDPI, vol. 4(2), pages 1-14, May.
    6. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    7. Dilupa Nakandala & Yung Po Tsang & Henry Lau & Carman Ka Man Lee, 2022. "An Industrial Blockchain-Based Multi-Criteria Decision Framework for Global Freight Management in Agricultural Supply Chains," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    8. Mojtaba Qolipour & Ali Mostafaeipour & Mohammad Saidi-Mehrabad & Hamid R Arabnia, 2019. "Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study," Energy & Environment, , vol. 30(1), pages 44-62, February.
    9. Weijun Wang & Dan Zhao & Liguo Fan & Yulong Jia, 2019. "Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine," Energies, MDPI, vol. 12(11), pages 1-21, June.
    10. de Almeida, Liliane & Augusto de Jesus Pacheco, Diego & Caten, Carla Schwengber ten & Jung, Carlos Fernando, 2021. "A methodology for identifying results and impacts in technological innovation projects," Technology in Society, Elsevier, vol. 66(C).
    11. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    12. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    13. Yong-Wu Zhou & Chuanying Chen & Yuanguang Zhong & Bin Cao, 2020. "The allocation optimization of promotion budget and traffic volume for an online flash-sales platform," Annals of Operations Research, Springer, vol. 291(1), pages 1183-1207, August.
    14. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    15. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
    16. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    17. Jane Haider & Zhirong Ou & Stephen Pettit, 2019. "Predicting corporate failure for listed shipping companies," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 415-438, September.
    18. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    19. Wang, Xiaojun & Chan, Hing Kai & Li, Dong, 2015. "A case study of an integrated fuzzy methodology for green product development," European Journal of Operational Research, Elsevier, vol. 241(1), pages 212-223.
    20. Junlong Peng & Jing Zhou & Fanyi Meng & Yan Yu, 2021. "Analysis on the hidden cost of prefabricated buildings based on FISM-BN," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.

    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:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03878-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.