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Sequentially updated Bayesian network models for resource-constrained project duration forecasting
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DOI：10.1016/j.asoc.2026.116591.png)
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Abstract
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• Proposed model updates forecasts using recalculated risks at each tracking point.
• Outperforms static model in projects with diverse topological characteristics.
• Benchmarked on artificial data and validated using empirical project datasets.
• Excels in parallel projects with high complexity and strict resource limitations.
• Benchmark methods perform better in serial and low-scarcity parallel projects.
Keywords:
Project management
Dynamic forecasting
Risk model
Earned value management
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