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.

中文翻译：

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用于项目组合风险预测的 GA-BP 神经网络建模

目的

项目组合风险（PPR）管理对于促进项目组合（PP）的顺利实施具有重要作用。准确的PPR预测有助于管理者在复杂的PP环境中及时应对风险。然而，包括风险发生概率和考虑项目相互作用的风险影响后果的准确PPR影响程度预测的研究是有限的。本研究旨在模拟 PPR 预测并扩展 PPR 预测工具。

设计/方法/途径

在这项研究中，作者建立了一个基于遗传算法和反向传播神经网络（GA-BPNN）与熵梯形模糊数相结合的 PPR 预测模型。然后，作者用真实数据验证了所提出的模型并获得了 PPR 影响度。

发现

测试结果表明，该方法的平均绝对误差为0.002，平均预测准确率为97.8%。前者减少了 0.038，而后者与原始 BPNN 模型的结果相比提高了 32.1%。最后，作者进行了指标敏感性分析，以识别关键风险以有效控制它们。

原创性/价值

本研究开发了一种混合 PPR 预测模型，该模型将 GA-BPNN 与熵梯形模糊数相结合。作者使用该模型预测 PPR 影响程度，包括考虑项目相互作用的风险发生概率和风险影响后果。结果提供了对小反刍兽疫管理的见解。