Abstract
In the era of Industry 4.0, the optimization of manufacturing systems is pivotal for achieving operational excellence, cost efficiency, and agile responsiveness. This paper explores advanced modeling techniques that enable intelligent decision-making, system simulation, and performance prediction in complex manufacturing environments. Methods including discrete-event simulation, system dynamics, agent-based modeling, and hybrid approaches are examined for their efficacy in solving real-world production challenges. The integration of digital twins, data-driven models, and AI-enhanced simulations further elevates modeling accuracy and strategic foresight. Through comparative analysis and application case studies, the study underscores how these techniques contribute to reducing waste, improving throughput, and enhancing system adaptability under dynamic market demands.
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