Machine Learning Applications for Production Scheduling Optimization

Abstract

Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational, often exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support more effective decision-making processes. The paper further reviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry.