Collaborative Optimization Algorithm for Multimodal Logistics System based on Artificial Intelligence

In view of the complexity and lack of coordination in transportation scheduling in multimodal logistics systems, this paper introduces a collaborative optimization algorithm for multimodal logistics systems based on artificial intelligence (AI). In order to achieve comprehensive optimization of transportation cost, time efficiency and resource utilization, a convolutional neural network (CNN) is used for demand forecasting, NSGA-II (Nondominated Sorting Genetic Algorithm II) based on genetic algorithm is used for multi-objective optimization, and the Q-learning algorithm in reinforcement learning is used for switching decisions between logistics modes. First, a collaborative optimization model of a multimodal logistics system is established, and CNN is used to accurately predict demand fluctuations and transportation status to provide data support for subsequent optimization. Secondly, the NSGA-II algorithm is used for multi-objective optimization to maximize the overall benefit of the system by selecting the optimal transportation path and resource allocation plan. Finally, the Q-learning reinforcement learning algorithm is used to adjust the switching strategy between different logistics modes to achieve flexible resource scheduling and further optimize the balance between transportation cost and time window. In multimodal logistics systems, the increase in “switching costs” and “switching time”, such as from 200,000 yuan to 400,000 yuan, and from 5 hours to 9 hours, highlights the impact of high-frequency mode switching on logistics systems. The AI-based collaborative optimization algorithm for multimodal logistics systems proposed in this study has achieved remarkable results in solving local optimal and collaborative problems in traditional scheduling methods, and has strong application potential and promotion value. In the future, real-time data and adaptive learning strategies can be combined to further improve the dynamic response capability and robustness of the system.