With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint vehicle dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results show that the proposed system is highly scalable and ensures a 100\% delivery success while maintaining low delivery-time and fuel consumption. The codes are available at https://github.com/LucasCJYSDL/DeepFreight.

翻译：随着货运需求与运输成本快速增长，如何通过智能车队控制实现高效且经济节约的解决方案成为重要问题。本文提出DeepFreight——一种基于无模型深度强化学习的多程货运配送算法，该算法包含两个紧密协作的组件：卡车调度与包裹匹配。具体而言，采用称为QMIX的深度多智能体强化学习框架学习调度策略，据此可针对货运请求获得车队的多步联合车辆调度决策。随后执行高效的多程匹配算法，将货运请求分配给卡车。此外，DeepFreight集成了混合整数线性规划优化器以进行进一步优化。评估结果表明，该系统具有高度可扩展性，在保持低配送时间与低油耗的同时确保100%的配送成功率。代码开源于https://github.com/LucasCJYSDL/DeepFreight。