Robust Optimization of Cold Chain Logistics Networks with Time Window Under Uncertain Demand: A Case Study in China

China's cold chain industry suffers from the problems of imperfect cold chain logistics networks and irrational planning, causing high cold chain loss and high enterprise costs, and severely restricting the development of the industry. Considering the high uncertainty of cold chain market demand, this paper adopts the robust optimization method to construct an optimization model of a cold chain logistics network with a time window by taking the minimization of the comprehensive cost of the system as the goal, and uses a genetic algorithm to solve the model. This paper takes Company A as an example for analysis, and obtains the optimization scheme through the optimization model. The location of the optimal distribution center is obtained, and six distribution paths are planned so as to form a complete cold chain network system. Meanwhile, the optimization results under different demand fluctuation risk levels and different freight conditions are considered, and the results show that the location selection and path scheduling will not change under different demand fluctuation levels. In addition, a sensitivity analysis of the dynamic change of freight rate was carried out. When the freight rate reaches $4.5/t/km, not only the transportation cost and cargo damage cost will increase accordingly, but also the routing scheme will change accordingly.