Mathematical Optimization and Forecasting of Nuclear Power Plant Construction Progress using Probabilistic Models and Machine Learning Methods
Yuriy Dmitrishin Personal website: https://dmitrishin.github.io/
This work presents a generalized framework for probabilistic forecasting and optimization of nuclear power plant construction projects under uncertainty.
The approach combines:
- PERT and GERT network models;
- Monte Carlo simulation;
- probabilistic critical and subcritical path analysis;
- decision-making criteria under risk and uncertainty;
- local and iterative schedule optimization;
- multidimensional project trajectory modeling;
- machine learning methods for forecasting.
The framework is intended to support predictive project management, scenario analysis, and adaptive optimization of complex nuclear construction projects.
If you use this work in research, software, project management methodology, or a commercial implementation, please cite the original publication:
Yuriy Dmitrishin, "Mathematical Optimization and Forecasting of Nuclear Power Plant Construction Progress using Probabilistic Models and Machine Learning Methods", 2018.
DOI: https://doi.org/10.5281/zenodo.15373676
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