Many-Objective Intelligent Scheduling Optimization Algorithm for Complex Integrated System

Due to the increasing consumer demand for custom products, aluminum alloy component creep forming manufacturing has shifted towards production modes designed for multiple varieties and small batches, leading to problems such as complex production organization and low production efficiency. In the specific case of modern large-scale aluminum alloy aerospace components, the manufacturing requirements cannot be satisfied. According to the production characteristics and process requirements in this industry, a many-objective, whole-process production scheduling model was established, and a residual rectangle-based many-objective evolutionary algorithm (RTEA) was developed to solve it effectively. The RTEA uses the residual rectangle method in the decoding phase for autoclave filling, which improves the productivity of the autoclave. We further designed a three-stage environmental selection strategy to strengthen the balance of convergence and diversity and increase the selection pressure in the evolutionary process. Computational experiments were performed using industrial datasets relative to aerospace components and engineering production data. The advantages and competitiveness of the comprehensive production scheduling model and the RTEA were verified, as evidenced by an increase in production line efficiency of 20%. In conclusion, the proposed approach offers an effective solution to the many-objective production scheduling problem hindering aluminum alloy creep forming component production.