A machine learning based EMA-DCPM algorithm for production scheduling.

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications. [ABSTRACT FROM AUTHOR]