Flexible time horizon project portfolio optimization with consumption and risk control

Liu, Yong Jun; Zhang, Weiguo

doi:10.1016/j.asoc.2018.12.017

Cited by 9 publications

(3 citation statements)

References 42 publications

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Section: Discussionmentioning

confidence: 99%

“…This study has three main theoretical implications: first, most of the existing quantitative studies on PPR consider risks as modeling objective functions or constraint conditions instead of calculating PPR values (Dixit and Tiwari, 2019;Liu and Zhang, 2019), fail to consider project interactions (Paquin et al, 2016), or neglect risk impact consequences (Ghasemi et al, 2018). This study combines risk occurrence probabilities with their impact consequences to predict PPR impact degrees based on project interactions, which enriches PPR prediction studies.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

“…To fill that gap and help managers better manage PP risks, it is necessary to develop a PPR prediction tool that can achieve improved PPR prediction accuracy in uncertain environments. In addition, previous studies mainly considered risks as constraint conditions or objective functions instead of calculating PPR values (Dixit and Tiwari, 2019;Liu and Zhang, 2019), predicted risks by combining risk occurrence probabilities with risk impact consequences regardless of project interactions (Paquin et al, 2016) or simply predicted risk occurrence probabilities while considering project interactions (Ghasemi et al, 2018). To the best of our knowledge, few studies on predicting the impact degrees of PPRs have considered both risk occurrence probabilities and risk impact consequences, as well as project interactions.…”

Section: Introductionmentioning

confidence: 99%

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GA-BP neural network modeling for project portfolio risk prediction

Bai

Wei

Zhang

et al. 2022

JEIM

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PurposeProject 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/approachIn 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.FindingsThe 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/valueThis 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.

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