Multi-objective optimization and simulation algorithm design for large-scale intelligent logistics networks

With the rapid development of e-commerce and intelligent logistics technology, the efficient operation of large-scale intelligent logistics network faces multiple challenges, such as path optimization, resource scheduling and cost control. In order to ensure the effectiveness and efficiency of the logistics network at the same time, a multi-objective optimization model is established with the objectives of minimizing the total cost of product supply and minimizing the total time of supply. The traditional single-objective optimization method is difficult to balance multiple key factors, and the multi-objective optimization algorithm has become one of the key technologies to improve the intelligence level of the logistics system. This paper focuses on the optimization problem of intelligent logistics network, constructs a multi-objective optimization model, combines genetic algorithm (GA), particle swarm optimization (PSO) and other intelligent optimization methods, and realizes the collaborative optimization of transportation path, distribution time and resource utilization. A logistics network simulation system based on multi-intelligent body simulation is designed and developed to evaluate the applicability and stability of the optimization algorithm in different scenarios. The multi-objective optimization method proposed in this paper can effectively reduce logistics costs, improve distribution efficiency, and enhance the adaptability and robustness of the system. The research results provide theoretical support and practical guidance for the intelligent regulation and optimization of large-scale intelligent logistics networks.