Multi-Objective Optimization of Energy Cost, Production Quality, and Equipment Degradation Using AI-Based Dynamic Models
Purpose: This study aims to develop a transparent AI-assisted dynamic optimization framework that simultaneously addresses energy and operating costs, production quality, and equipment degradation in industrial manufacturing systems.
Research Methodology: The study employs a mathematical modeling and illustrative simulation approach integrating AI-based prediction, coupled dynamic state models, uncertainty-aware feasibility constraints, and Pareto-based multi-objective optimization. A reproducible simulation design is specified for a 24-hour horizon comprising 96 fifteen-minute decision intervals, supported by objective normalization and a staged validation framework.
Results: The proposed framework illustrates how energy costs, quality deviations, and equipment degradation can be evaluated simultaneously while maintaining operational feasibility. The illustrative simulations demonstrate the conceptual trade-offs among competing objectives and highlight the roles of uncertainty margins, Pareto-optimal policy selection, and dynamic feasibility assessment. All numerical examples are synthetic and do not represent experimentally validated industrial performance.
Conclusions: Integrating AI-based prediction with dynamic modeling and Pareto optimization provides a structured approach to balancing operational efficiency, product quality, and equipment reliability. The framework emphasizes transparent decision-making, operational constraints, and systematic validation before industrial implementation.
Limitations: The study relies on synthetic scenarios without empirical factory data. Its simplified dynamic models, uncertainty assumptions, and optimization procedures require calibration and validation under actual industrial operating conditions.
Contributions: This study advances AI-assisted industrial optimization by integrating multi-objective decision modeling, uncertainty-aware feasibility assessment, and Pareto-based policy selection into an auditable framework. It also proposes a staged validation protocol to support responsible and reproducible industrial deployment.
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