Low-carbon Distribution Path Optimization of Urban Fresh Logistics Based on Improved Ant Colony Algorithm
Abstract
With the rapid expansion of fresh food e-commerce and urban instant retail, urban fresh logistics has become a core component of modern urban circulation system. Different from general commodity logistics, fresh cold chain distribution has strict requirements on delivery time, transportation temperature and service quality, which also brings problems such as repeated vehicle routes, high energy consumption and excessive carbon emissions in actual operation. Traditional fresh logistics path optimization mostly takes the shortest distance or the lowest transportation cost as the single optimization goal, ignoring the low-carbon development requirements under the dual-carbon policy, and the classic optimization algorithm is prone to local optimal solutions and slow convergence speed in complex urban traffic scenarios, resulting in poor practicability of optimization results. Aiming at the above pain points, this paper constructs a multi-objective optimization model of urban fresh logistics low-carbon distribution, which takes total distribution cost and total carbon emission as dual optimization objectives, and sets multi-dimensional constraints including vehicle load limit, customer time window and driving speed limitation. On this basis, an improved ant colony algorithm is proposed by optimizing the pheromone update mechanism and heuristic function weight, which effectively makes up for the defects of the traditional ant colony algorithm. Finally, simulation example analysis is carried out with urban fresh distribution scene data. The results show that compared with the traditional algorithm optimization scheme, the improved algorithm can reduce the total distribution cost by 8.2% and the total carbon emission by 11.5%, and significantly improve the convergence efficiency and path rationality. This research realizes the coordinated optimization of economic benefit and environmental benefit of fresh logistics distribution, and can provide effective theoretical support and practical reference for the intelligent and low-carbon transformation of urban fresh logistics enterprises.