GA-BP neural network modeling for project portfolio risk prediction
Journal of Enterprise Information Management
ISSN: 1741-0398
Article publication date: 18 November 2022
Issue publication date: 13 May 2024
Abstract
Purpose
Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.
Design/methodology/approach
In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.
Findings
The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.
Originality/value
This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China [grant number 72002018, 72201040]; Social Science Planning Fund of Shaanxi Province [grant number 2020R028]; Innovation Capacity Support Plan of Shaanxi Province [grant number 2020KJXX-054]; Ministry of Education Humanities and Social Sciences Fund [grant number 17XJC630001]; Youth Innovation Team of Shaanxi Universities [grant number 21JP009, 22JP003]; Project funded by China Postdoctoral Science Foundation [grant number 2021M700527]; and the Fundamental Research Funds for the Central Universities [grant numbers 300102231639, 300102230613, 300102232607].
Citation
Bai, L., Wei, L., Zhang, Y., Zheng, K. and Zhou, X. (2024), "GA-BP neural network modeling for project portfolio risk prediction", Journal of Enterprise Information Management, Vol. 37 No. 3, pp. 828-850. https://doi.org/10.1108/JEIM-07-2022-0247
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited