The Vehicle Routing Problems with Time Windows (VRPTW) has remained a classic and continuously studied problem since its introduction. With the rapid growth of cold chain product distribution demands, VRP research has become increasingly important for guiding real-world scheduling decisions. However, most studies focus on further subdividing new scenarios and constraints, often overlooking fundamental real-world applications. This includes the impact of unknown road conditions on costs, rough cost modeling, and poor algorithm adaptability to high-dimensional cold chain constraints. To address these three issues, this paper proposes the Spatio-temporal dependency and road network distribution-based traffic forecasting model (STD-RND) to provide region-level traffic scheduling information. The model also constructs cost functions to quantify cargo spoilage, refrigeration, and carbon emissions. Finally, we introduce an Improved Hippo Optimization with Traffic Forecasting (IHTF) that incorporates traffic prediction to enhance the solution quality of the VRPTW in cold chain scenarios. To strengthen optimization performance and prevent premature convergence to local optima, we integrate several enhanced strategies, including chaotic mapping, dynamic Cauchy mutation, and an escape mechanism. Through a series of experiments on the Solomon dataset and simulation datasets based on real road networks, we demonstrate that the proposed algorithm shows consistent superiority and effectiveness.
Wang et al. (2026) studied this question.