DeepFreight：融合深度强化学习与混合整数规划的多转运卡车货运配送
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery
AI总结：
本文提出DeepFreight，结合QMIX深度多智能体强化学习、多转运匹配算法和混合整数线性规划优化器，实现多转运卡车货运配送的高效调度，确保100%配送成功率并降低配送时间和燃油消耗。
摘要：
随着货运配送需求和运输成本的快速增长，对车队进行智能控制以实现高效且成本合理的解决方案成为一个重要问题。本文提出DeepFreight，一种基于无模型深度强化学习的多转运货运配送算法，包含两个紧密协作的组件：卡车调度和包裹匹配。具体而言，利用名为QMIX的深度多智能体强化学习框架学习调度策略，据此可针对配送请求获得车队的多步联合车辆调度决策。然后执行一种高效的多转运匹配算法，将配送请求分配给卡车。此外，DeepFreight与混合整数线性规划优化器集成，以进行进一步优化。评估结果表明，所提出的系统具有高度可扩展性，在保持较低配送时间和燃油消耗的同时，确保100%的配送成功率。代码可在https://github.com/LucasCJYSDL/DeepFreight获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
