In complex logistics,addressing the vehicle routing problem(VRP)with simultaneous pickup and deliv-ery and time windows,an NP-hard problem,becomes increasingly challenging as the scale expands.Traditional heuristic methods,often unable to leverage prior optimization knowledge,result in slow convergence.To address this,we introduce a multitask-based evolutionary algorithm(MBEA),which assists the optimization of the original large-scale problem by constructing multiple simple and similar subtasks and utilizing transfer learning to acceler-ate convergence speed.First,a subset of orders is randomly selected from the original problem to construct various subtasks,and then a multitask evolutionary approach is applied to generate candidate solutions for the original problem and subtasks.Given that the subtasks are simpler yet similar to the original problem,useful routing traits can be shared through knowledge transfer among the tasks,thereby speeding up its evolutionary search.To valid-ate MBEA's efficacy,empirical studies were conducted on a large-scale express dataset from Jingdong,and the res-ults demonstrate that MBEA outperforms recently proposed vehicle routing algorithms.
关键词
车辆路径规划问题/时间窗约束/同时取送货/进化算法/迁移优化
Key words
Vehicle routing problem(VRP)/time windows constraint/simultaneous pickup and delivery/evolution-ary algorithm/transfer optimization
维普
万方数据