带软硬时间窗的车载无人小车协同配送路径优化  

Collaborative Delivery Routing Optimization for Truck-mounted Unmanned Vehicles Considering Soft and Hard Time Windows

作　　者：杨卓 叶艳[1] Yang Zhuo;Ye Yan(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学机电工程学院,广州510006

出　　处：《机电工程技术》2024年第11期76-82,共7页Mechanical & Electrical Engineering Technology

摘　　要：研究带软硬时间窗的车载无人小车协同配送路径问题,卡车在停靠点释放回收无人小车,在考虑包裹质量、体积的基础上,小车必须在硬时间窗范围内为客户配送包裹,且在小的软时间窗外服务时带有惩罚成本。针对该问题,构建以行驶成本和时间窗惩罚成本之和最小为目标函数的整数规划模型,并在大规模邻域搜索算法(Large Neighborhood Search,LNS)的基础上引入一种自适应机制,设计一种改进的自适应大邻域搜索算法(Adaptive Large Neighborhood Search,ALNS)进行求解。对比ALNS、LNS和遗传算法(Genetic Algorithm,GA)在调整后的Solomon算例中的求解表现。结果表明,所提算法的解质量更好,相对于LNS、GA的平均优势分别是14.6%、32.4%,且能在更短的时间内得到最优结果,求解效率良好。所设计的算法能解决带软硬时间窗的车载无人小车协同配送路径问题。The collaborative delivery routing problem of truck-mounted unmanned vehicles considering soft and hard time windows is researched.Trucks release and recover unmanned vehicles(UVs)at parking points.UVs must deliver packages to customers within their hard time windows and there are penalty costs when serving outside the small soft time windows,when the weight and volume of packages are considered.A mixed integer programming model is established for the problem,whose objective function is the minimization of the sum of traveling costs and costs of time window penalty.An adaptive large neighborhood search(ALNS)is created by adding an adaptive mechanism into large neighborhood search(LNS)to solve the model.The performances of ALNS,LNS,and genetic algorithm(GA)in the adjusted Solomon example are compared.The results show that the quality of solution of the designed algorithm is better than LNS and GA,with an average advantage of 14.6%compared to LNS and an average advantage of 32.4%compared to GA.It can obtain the optimal result in shorter time and has good solving efficiency.The designed algorithm is able to solve collaborative delivery routing problem of truck-mounted unmanned vehicles considering soft and hard time windows.

关 键 词：车载无人小车 时间窗 路径优化 多行程协同配送 自适应大邻域搜索算法 

分 类 号：U492.2[交通运输工程—交通运输规划与管理]