Seasonal Cold chain multimodal transport robust optimization considering transportation time uncertainty

Multimodal transportation in cold chains plays a crucial role in modern logistics. However, optimizing transportation routes becomes a complex and critical task in the face of challenges such as seasonal temperature variations and transportation time uncertainty. This study focuses on the optimization of transportation routes in cold chain multimodal transportation systems, considering seasonal temperature variations and uncertainties in transportation time. A scenario-based robust optimization model is constructed, and the maximum regret value is introduced to limit the conservativeness of the solutions. Based on the cold chain transportation from Chengdu to Shanghai, experimental results demonstrate that as temperatures rise, refrigeration costs increase, leading to a rise in total costs, while transportation time gradually decreases. Transportation time uncertainty significantly impacts transportation costs, necessitating the adoption of more conservative strategies to mitigate potential risks. Additionally, the setting of the maximum regret value has varying effects on transportation costs under different temperature conditions. In scenarios with moderate temperature differences, adjusting the maximum regret value can effectively control costs and enhance transportation efficiency. However, in scenarios with very low or very high temperature differences, changes in the maximum regret value do not alter the optimal solution. In conclusion, this research provides valuable insights for optimizing cold chain multi-modal transportation systems and offers beneficial guidance for related management decisions.