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Considering the size (length, width, and height) and weight of massive cargos and on the basis of satisfying the throughput capacity of the road network, a massive-cargo transportation routing model was proposed to find the shortest transportation route based on the origin and destination. According to the characteristics of the routing problem, a reinforcement learning algorithm was designed to solve the problem and determine the strategy, reward function, value function, and environment model. Additionally, based on a case study, an optimized transportation plan was developed and the model sensitivity was analyzed to verify the validity and correctness of the proposed model and algorithm.
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