Research on Integrated Collaborative Optimization of Digital Supply Chain Logistics Information in Smart Port Construction

This paper proposes an integrated collaborative optimization model, which aims to optimize port supply chain management through precise integration and collaborative scheduling of logistics information. First, an algorithm framework based on big data analysis and machine learning is designed to integrate the port's logistics data, transportation information, inventory data and ship scheduling information to achieve real-time information sharing and precise scheduling. Secondly, a supply chain collaborative optimization model is constructed, considering factors such as resource constraints, transportation timeliness and cost minimization, and the model is solved by simulated annealing algorithm and particle swarm optimization algorithm. The simulation results show that the optimized system can significantly improve the port's throughput, reduce logistics costs, and show good adaptability and stability in various scenarios. Accurate data analysis results show that under the condition of controlling logistics resources, the optimized solution reduces the operating cost by about 15% compared with the traditional solution, and shortens the cargo processing time by about 10%.