主讲人

王凯

讲座时间

2024年4月24日（周五）14:30

讲座地点

25教A206

01 主讲人介绍

王凯，清华大学车辆与运载学院副教授、博士生导师。此前分别就职于麻省理工学院（博士后）以及卡耐基梅隆大学（研究员）。主要研究方向为智能交通与物流系统；主持和参与基金委重点、国际合作交流项目等。

02 讲座内容

电商发展催生高效末端配送需求，本文聚焦公交-无人机协同配送优化，基于时空网络构建模型，采用多变量生成算法提出混合求解策略；深圳实测验证该方法较基准方案提速最高 5 倍，算法具备可扩展性、高效性与鲁棒性，适配实际城市物流场景。

The surge in e-commerce has heightened the need for efficient last-mile delivery, prompting the exploration of drone-assisted logistics integrated with public transportation. This paper focuses on optimizing delivery costs in the Drone-Transit Coordinated Delivery Problem (DTCDP) by formulating mathematical models based on time-space networks. We use a multi-variable generation (MVG) algorithm that iteratively solves a sparse master problem and a subproblem to identify promising paths. For solving instances of varying scales, we developed a hybrid solution strategy: an exact algorithm combining a branch-and-bound framework with MVG, accelerated by cut generation and heuristic primal bounds, is applied to small and medium-sized cases; for large-scale cases, a heuristic method that solves a compact integer program after MVG pre-processing is employed. Large-scale experiments based on real-world data from Shenzhen demonstrate that our approach significantly outperforms benchmark methods, achieving speedups of up to 5x, alongside enhanced stability. Sensitivity analysis quantifies the effects of key parameters, including transit network scale, vehicle scheduling, drone charging rates and endurance, and network topology. The algorithm and model exhibit scalability, efficiency, and robustness, making them suitable for real-world urban logistics applications.