A novel approach for product makespan prediction in production life cycle

摘要

A novel self-adaptive immune genetic algorithm (SAIGA)-dynamic back propagation neural network (DBPNN) model was developed to solve the difficulty of making maximum completion time prediction (makespan prediction). By analyzing history data of makespan and its related factors in production life cycle, a prediction model based on back propagation (BP) neural network was established, weight values and threshold values of the BP neural network model were improved dynamically, and the DBPNN model was further optimized via SAIGA, thus obtaining the SAIGA-DBPNN model. The proposed SAIGA-DBPNN model was applied to an aviation enterprise's makespan prediction. The application case suggested that the novel prediction method can yield accurate results. The usefulness of accurate makespan prediction in production life cycle as a tool for improving production efficiency was highlighted.