Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation.

To solve the multimodal transport route optimization problem considering carbon emission, the vehicle speed has time-varying characteristics, and the customer has a time window limit. The carbon emission of multimodal transport system is affected by the energy consumption of transport vehicles in the time-varying network. The time-varying network is uncertain, and carbon emissions may continue to rise after a gradual decline. Based on this, this study established the sum of the carbon emission cost, transportation cost, penalty cost for exceeding the time window, and the damage cost of the cold chain cargo as the objective function. A route optimization model of cold chain container multimodal transportation was established. Static and dynamic optimization scenarios were designed and a hummingbird evolutionary genetic algorithm was used to solve the model. The effectiveness of the model and the practical value of the study are verified by the empirical analysis of the multimodal transport network of the Yangtze River Delta economic group. Results show that the dynamic model of the time-varying network can more truly reflect the transportation of the multimodal transport network and meet the efficiency requirements for the cold chain container multimodal transport. This study aims to solve the time-varying network under cold chain route optimization of container intermodal transportation, provides new insights for related businesses and a theoretical basis for reasonable multimodal transport route decisions.

In order to study the mixed time window vehicle routing optimization problem based on customer priority, a customer differentiation management strategy based on customer priority is proposed. Combined with the main factors affecting customer priority evaluation and the characteristics of vehicle routing problem with mixed time windows, a comprehensive evaluation index affecting customer priority was first established and DBSCAN clustering algorithm was used for clustering analysis of customer priority to solve the optimization problem of cold chain distribution route considering customer priority. Fuzzy time window of refrigerated vehicles was then constructed with trapezoidal fuzzy number, and a mathematical programming model was built with an objective function for minimizing the sum of fixed, green, penalty, refrigeration, and cargo damage costs. Two scenarios of out-of-stock and not-out-of-stock were designed. Finally, an improved genetic algorithm was used to solve the model, and the rationality of the model was verified through a case of imported fruit distribution in Xiamen City. Results showed that the proposed method can effectively solve the routing problem of refrigerated trucks considering customer priority. Moreover, the findings of this study can provide a new approach for solving the routing optimization problem of refrigerated trucks considering customer priority.