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This study introduces a hybrid approach for optimizing project scheduling by integrating Taguchi methods, the Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI)-based predictive modeling. Unlike deterministic methods such as Critical Path Method (CPM), our approach accounts for variability in task durations. A generic example is used to demonstrate the methodology, as sourcing real-world data remains challenging. Statistical validation, including R2 scores, confidence intervals, and feature importance analysis, confirms the robustness of the AI-driven optimization. This framework enhances project planning by reducing duration variability and improving resource allocation strategies.

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Optimization of Critical Path in Project Management Using Traditional, Taguchi, and AHP Methods

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  • 发表日期：

    2025年

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  • Rahul Basu

摘要

This study introduces a hybrid approach for optimizing project scheduling by integrating Taguchi methods, the Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI)-based predictive modeling. Unlike deterministic methods such as Critical Path Method (CPM), our approach accounts for variability in task durations. A generic example is used to demonstrate the methodology, as sourcing real-world data remains challenging. Statistical validation, including R2 scores, confidence intervals, and feature importance analysis, confirms the robustness of the AI-driven optimization. This framework enhances project planning by reducing duration variability and improving resource allocation strategies.