Optimization of Production Scheduling in Smart Manufacturing Environments Using Machine Learning Algorithms

Abstract

The transition to Industry 4.0 has introduced smart manufacturing environments, where dynamic processes require real-time decision-making to optimize production scheduling and enhance operational efficiency. This study aims to develop and implement advanced machine learning (ML) algorithms for optimizing production scheduling in smart manufacturing environments, focusing on improving efficiency, resource allocation, and adaptability under dynamic conditions. A hybrid ML model combining reinforcement learning (RL) and genetic algorithms (GA) was developed. Historical and real-time data from a simulated smart factory were analyzed. The model trained on 500 iterations of production scenarios involving dynamic demand, machine availability, and workforce constraints. Performance was benchmarked against traditional heuristic scheduling methods to validate improvements in key performance indicators. The hybrid ML model delivered significant improvements over traditional methods. Production efficiency increased by 39%, resource utilization reached 91% (a 14% improvement), and machine downtime was reduced by 34%. The scheduling system achieved a 94% success rate in meeting delivery deadlines under varying scenarios, compared to 78% using heuristic methods. Energy consumption per task was reduced by 17%, reflecting enhanced sustainability. In large-scale tests involving 1,000 tasks, the model maintained over 96% operational efficiency, confirming its scalability and robustness. The integration of ML in production scheduling demonstrates transformative potential for smart manufacturing environments, offering enhanced efficiency, adaptability, and sustainability. The proposed hybrid ML model represents a scalable, data-driven solution tailored to Industry 4.0 requirements.