Optimization Vehicle Routing for Cold Chain Logistics in Time-Varying Networks Using an Improved Ant Colony Algorithm

Optimizing the route of cold chain logistics vehicles can reduce the loss of goods in transit. In actual roads, there is a direct correlation between road conditions and driving speed, thus the driving speed has a time-varying characteristic. A mathematical analysis model for optimizing the routing of cold chain logistics vehicles in a time-varying network was established in this paper. The correlation of travelling time and road conditions were considered. Taking vehicle travel time, time window cost, and vehicle activation cost as optimization objectives, various constraints were analyzed, and an optimization model was constructed. The vehicle path model belongs to NP-hard problems, and the solving time will increase exponentially with the expansion of the problem scale. The improved ant colony algorithm (IACO) was used to solve the model. The pheromone concentration update method was optimized, and the implementation steps of the algorithm was optimized, too. Simulation results verified the algorithm effectiveness.