Self-Planning Method for Global Path of Logistics Trolley Considering Task Requirements

In order to improve the scheduling ability of urban cold chain multi-series distributed logistics, it is necessary to carry out path optimization planning and design. This paper puts forward the shortest path optimization planning algorithm of urban cold chain multi-series distributed logistics based on particle swarm optimization. The particle swarm optimization method is adopted to sample the environmental information of urban cold chain multi-serial point distributed logistics area, the collected data of urban cold chain multi-serial point distributed logistics area is dynamically weighted and the shortest path optimization control is carried out, and the path space area grid block planning detection model of urban cold chain multi-serial point distributed logistics area is established. According to the task requirements, Particle swarm optimization (PSO) shortest path detection method is used to optimize the shortest path planning and block search of urban cold chain multi-series distributed logistics. The pheromone features of the shortest path planning of urban cold chain multi-series distributed logistics are extracted. The shortest path planning method is used to analyze the characteristics of urban cold chain multi-series distributed logistics, and the global evolution game features of logistics trolley are analyzed. Particle swarm optimization (PSO) algorithm is used to carry out adaptive optimization in the shortest path planning process of urban cold chain multi-series distributed logistics, so as to realize independent planning and shortest optimization of the global path of urban cold chain multi-series distributed logistics. The simulation results show that the shortest path planning of urban cold chain multi-series distributed logistics with this method has good optimization ability, which improves the response ability of urban cold chain multi-series distributed logistics and reduces the cost of distribution time.