Distribution Estimation Algorithm for Cloud Manufacturing Scheduling Optimization

Abstract

To address the resource scheduling problem in complex production environments, this study proposes a production scheduling model based on Estimation of Distribution Algorithm.The model constructs a probability model using spatial distribution, evaluates the scheduling population based on high-quality individuals, introduces an archive mechanism to enhance solution diversity, and combines Deep Reinforcement Learning and Tabu Search algorithm for global optimization.It achieves adaptive production scheduling optimization under dynamically changing resources.In testing experiments, the model achieves an accuracy of 95.11 % in sample classification prediction tasks.The computational load and number of parameters for production data processing are 664.8FLOPs and 90.54 M, respectively.The scheduling delay rate and resource utilization are 4.39 % and 97.96 %, significantly outperforming comparison models.These results indicate that the model provides stable and efficient production scheduling optimization and multi-constraint conditions, offering reliable algorithm support for production scheduling in cloud-based networked manufacturing environments.