Optimization Method for Supply Chain Logistics Network Structure in Intelligent Manufacturing System

The research focuses on the optimization of closed-loop supply chain (CLSC) network design in intelligent manufacturing systems, to achieve sustainable development goals by optimizing resource utilization, and reducing environmental impact and operating costs. The research focuses on constructing and optimizing supply chain networks, and the proposed algorithm is applied to multi-objective optimization of intelligent manufacturing supply chains. The experimental results show that the optimized supply chain efficiency has increased from about 81% before optimization to 96.13%. The total cost is controlled between 2.30654 and 3.38452 million yuan, which is 8%-12% lower than traditional methods. The profits are maximized to 1.42387 million yuan. The utilization rates of raw materials and remanufactured materials have increased to 83.78% and 69.21%, respectively. Energy consumption is controlled between 1442.672 and 1524.567 MWh. Carbon emissions are reduced to 126.564 tons. The highest product recycling rate is 74.52%. The total logistics distance of the supply chain is optimized from 1147.932 to 1389.432 km, and the production cycle is shortened from 44.982 to 49.782 h. By modeling and optimizing parameters such as logistics nodes, production cycles, and utilization rates of raw and remanufactured materials, the algorithm proposed by the research performs excellently, with a stable running time ranging from 18.732 to 19.564 s. The significance of the research lies in providing a dynamic, efficient, and sustainable CLSC design model for intelligent manufacturing systems, and demonstrating the significant advantages of this method in improving supply chain efficiency, optimizing cost control, and achieving environmental sustainability.