Collaborative scheduling algorithm for full quantity materials based on process and machine learning

Participants in the supply chain may have different information, leading to incomplete or inaccurate information when making decisions. To this end, a process and machine learning based collaborative scheduling algorithm for all materials is proposed. Design a health monitoring process for material supply chain based on R-tree dynamic indexing algorithm. Based on this, artificial neural networks in machine learning are applied to mine the data of the entire material supply chain. Through data mining, various data in the supply chain can be integrated and analyzed to improve information transparency and accuracy, and reduce information asymmetry. Adopting a dual layer scheduling model to achieve dual layer collaborative scheduling of materials. The experimental results show that the research method effectively improves the accuracy of data mining in the entire material supply chain, and the utilization rate of materials under this method is always higher than 95%.