摘要
为了给各物流企业在车辆配送路径规划方面提供合理有效的决策支持,提出了一种多区域混合采样策略的全局搜索和基于个体间路线序列差异局部搜索相结合的混合进化多目标优化算法。对问题进行合理的数学模型构建,利用全局搜索策略使得种群个体从多个方向快速收敛至Pareto前沿面,并使用局部搜索策略来引导种群中表现差的个体朝着表现好的个体的方向进化,从而提高了个体的质量和算法的局部搜索能力。所提算法在集配一体化车辆路径问题的标准测试数据集上进行了一系列的实验,结果表明所提方法在收敛性上明显提升,同时搜索到的解具有良好的分布性能。
In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribution route planning,a hybrid evolutionary multi-objective optimization algorithm combining a multi-region mixed-sampling strategy for global search and a local search based on individual route sequence differences is proposed for the problem.A reasonable mathematical model is constructed and the global search strategy is used to make the population individuals to converge quickly to the Pareto front from multiple directions,and the local search strategy is employed to guide the poorly performing individuals in the population to evolve towards the direction of better performing individuals,thus improving both individuals quality and local search capability of the algorithm.By conducting a series of experiments on a standard benchmark of vehicle routing problem with simultaneous delivery and pickup and time windows(VRPSDPTW),and experimental results show that the proposed method significantly improves the convergence performance and produces solutions with good distribution.
作者
张闻强
王晓萌
张晓晓
张国辉
Zhang Wenqiang;Wang Xiaomeng;Zhang Xiaoxiao;Zhang Guohui(Henan University of Technology,Zhengzhou 450001,China;Zhengzhou University of Aeronautics,Zhengzhou 450001,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第8期1914-1928,共15页
Journal of System Simulation
基金
国家自然科学基金联合基金(U1904167)
河南省重点研发与推广专项(科技攻关)(232102211049)
郑州市科技局自然科学项目协同创新专项(21ZZXTCX19)。
关键词
集配一体化
时间窗
混合进化算法
多区域采样策略
多目标优化
simultaneous delivery and pickup
time windows
hybrid evolutionary algorithm
multiregion sampling strategy
multi-objective optimization
