An explainable data-driven optimization framework for industrial predictive maintenance scheduling

Unexpected equipment failures in industrial systems can lead to significant production downtime, increased operational costs, and reduced asset life cycles. Leveraging recent advancements in data science and intelligent decision-support systems, this paper proposes an explainable data-driven optimization and mathematical modeling framework for predictive maintenance scheduling in industrial environments. The framework integrates sensor-based condition monitoring data with machine learning models to forecast potential equipment failures before they occur. A supervised learning approach, implemented using gradient boosting and temporal feature engineering, predicts the remaining useful life (RUL) and failure probability of critical assets. An explainability layer, based on SHAP (SHapley Additive exPlanations) values, provides interpretable insights into the most influential factors contributing to predicted failures, enabling maintenance engineers to validate model outputs and trust automated recommendations. The predictive outputs are then embedded into a mixed-integer linear programming (MILP) model to generate optimal maintenance schedules that minimize total downtime and maintenance costs while satisfying operational constraints such as resource availability and production deadlines. The proposed framework is validated using a combination of publicly available predictive maintenance datasets and real-world industrial sensor data. Experimental results demonstrate a reduction of up to 22% in unplanned downtime and 15% in maintenance costs compared to reactive and preventive maintenance strategies, while maintaining high interpretability for domain experts. This integrated approach highlights the potential of combining explainable AI, predictive analytics, and mathematical optimization for sustainable and efficient industrial operations.