This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the ...This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.展开更多
针对Leach(low energy adaptive clustering hierarchy)协议在大规模网络中存在着数据传输效率不高和网络生命周期短的问题,提出了一种LEACH-CM-NGO优化算法。该方法通过在簇头选取阶段优化簇头数在所有节点中占比,引进能量密度因子和...针对Leach(low energy adaptive clustering hierarchy)协议在大规模网络中存在着数据传输效率不高和网络生命周期短的问题,提出了一种LEACH-CM-NGO优化算法。该方法通过在簇头选取阶段优化簇头数在所有节点中占比,引进能量密度因子和能耗因子改进阈值公式优化簇头分布,并在数据传输阶段,由原本的单跳传输改为多跳方式传输数据,引入基于立方映射方法,自适应权重策略和柯西变异的北方苍鹰优化算法改进簇头间数据传输路径,以提高网络的能效和数据传输效率。仿真结果表明,所提出的方法在减少能耗的同时,显著延长了网络的生命周期并提高了数据传输的成功率。展开更多
针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and im...针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and improved large neighborhood search algorithm,K-means-ILNSA)。采用先聚类后优化的策略,利用K-means算法对待配送客户进行分组,以提高优化效率。采用遗传算法对聚类产生的每组客户进行单独优化,以初步规划配送路径。引入大规模邻域搜索(large neighborhood search,LNS)算法对配送路径进一步优化,以有效避免算法陷入局部最优解。实验结果表明:所提算法能够有效解决带时间窗的车辆路径问题,其生成的车辆总路程短,优化求解效率高。展开更多
为了解决高比例新能源地区电网中新能源不确定性所导致的N-1故障线路过载问题,提出一种计及新能源不确定性并应用混合型潮流控制器(hybrid power flow controller,HPFC)控制模式的电网潮流优化方法。首先,建立了适应于多线路控制的HPFC...为了解决高比例新能源地区电网中新能源不确定性所导致的N-1故障线路过载问题,提出一种计及新能源不确定性并应用混合型潮流控制器(hybrid power flow controller,HPFC)控制模式的电网潮流优化方法。首先,建立了适应于多线路控制的HPFC稳态计算模型,并给出了在不同控制模式下的HPFC运行约束条件。其次,以电网有功网损和线路负载率指标为目标函数,考虑N-1安全约束和HPFC运行约束,建立应用HPFC控制模式的电网潮流优化模型。然后,通过模糊C均值聚类获取反映新能源出力、负荷不确定性的场景集合,并采用多目标多元宇宙优化算法(multi-objective multi-verse optimization,MOMVO)求解所提优化模型。最后,将所提潮流优化方法应用于江苏南通某地区电网。结果表明,所提方法能有效提高电网的经济性与静态安全性,且计算结果具有较好的稳定性。展开更多
基金supported by the National Natural Science Foundation of China (61963022,51665025,61873328)。
文摘This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.
文摘针对Leach(low energy adaptive clustering hierarchy)协议在大规模网络中存在着数据传输效率不高和网络生命周期短的问题,提出了一种LEACH-CM-NGO优化算法。该方法通过在簇头选取阶段优化簇头数在所有节点中占比,引进能量密度因子和能耗因子改进阈值公式优化簇头分布,并在数据传输阶段,由原本的单跳传输改为多跳方式传输数据,引入基于立方映射方法,自适应权重策略和柯西变异的北方苍鹰优化算法改进簇头间数据传输路径,以提高网络的能效和数据传输效率。仿真结果表明,所提出的方法在减少能耗的同时,显著延长了网络的生命周期并提高了数据传输的成功率。
文摘针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and improved large neighborhood search algorithm,K-means-ILNSA)。采用先聚类后优化的策略,利用K-means算法对待配送客户进行分组,以提高优化效率。采用遗传算法对聚类产生的每组客户进行单独优化,以初步规划配送路径。引入大规模邻域搜索(large neighborhood search,LNS)算法对配送路径进一步优化,以有效避免算法陷入局部最优解。实验结果表明:所提算法能够有效解决带时间窗的车辆路径问题,其生成的车辆总路程短,优化求解效率高。
