耦合充电模式下电动车辆路径优化建模与仿真OA
MODELING AND SIMULATION OF ELECTRIC VEHICLE ROUTING OPTIMIZATION IN COUPLED CHARGING MODE
针对公共充电和合约充电的耦合充电模式,建立电动物流车配送路径的混合整数规划模型,设计基于充电站构造子路径的序列插入解码算法,进一步采用遗传算法优化配送路径.仿真实验表明,相比于 CPLEX,基于序列插入解码的遗传算法兼具准确性和高效性;相比于公共充电,耦合充电模式下配送里程平均降低3.65%,配送成本平均降低3.65%,充电成本平均降低16.86%,合约成本平均增加8.29%,总成本平均降低6.85%.
Aimed at coupled charging mode for public charging and contract charging,a mixed-integer linear program is established for the optimization of electric logistics vehicles distribution path,a sequence-insertion decoding algorithm is designed based on the construction of sub-paths by charging stations,and the genetic algorithm is used to optimize the distribution path.Numerical experiments show that compared with CPLEX,the genetic algorithm based on sequence insertion decoding has advantages in terms of accuracy and efficiency.Compared with public charging,the coupled charging mode can reduce the average delivery mileage by 3.65%,the average delivery cost by 3.65%,the average charging cost by 16.86%,and the average contract cost increase by 8.29%.The average total cost can be reduced by 6.85%.
徐英;胡志华;李姚娜
上海海事大学物流研究中心 上海 201306上海海事大学物流研究中心 上海 201306上海海事大学物流研究中心 上海 201306
信息技术与安全科学
充电模式序列插入算法遗传算法电动物流车路径优化
Charging modeSequential insert heuristicsGenetic algorithmElectric logistics vehicleRouting op-timization
《计算机应用与软件》 2026 (9)
39-45,127,8
国家自然科学基金项目(71871136)上海市自然科学基金面上项目(23ZR1426500).
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