基于地铁的地上地下闭环物流配送路径优化

Distribution Routing Optimization of Ground-Underground Closed-Loop Logistics Based on Metro Network

面对闭环物流需求增长与道路资源紧缺的不平衡问题,设计了基于地铁的地上地下配送模式,以运输成本,时间窗惩罚成本,碳排放成本,转运成本,固定成本和地铁货运运营成本的总成本最小为目标函数,构建基于地铁的地上地下闭环物流配送路径优化模型。基于半初始化子路径扰动策略改进了自适应遗传算法,并以南京市秦淮区为例进行分析。结果表明:相比于常规自适应遗传算法,改进的算法可以缩短49.14%的运算时间,且求解更高效、稳定;选取多编组的开行方式,并设置物流车厢容量和发车间隔分布为3 000件和5 min,可以降低4.6%的平均服务时间和3.8%的总成本,具有较好的送达时效性和经济性;相比传统物流模式,合理利用现有地铁线网的地上地下闭环物流配送方案可以降低57.5%的碳排放量、减少8.1%的平均配送时间,其配送模式可为未来城市物流与地铁的发展提供参考依据。

In order to alleviate the imbalance between the closed-loop logistics demand growth and the road resources limit, a ground-underground model based on metro network was designed, and a ground-underground closed-loop logistics distribution optimization model was constructed to minimize transportation time cost, time window penalty cost, carbon tax, transshipment cost, vehicle fixed cost and subway station freight operation cost.Based on the semi-initialized and sub-path disturbance strategy, the adaptive genetic algorithm was improved.The implementation effect was evaluated based on the Qinhuai District, Nanjing. The results show that firstly, the improved adaptive genetic algorithm can shorten the calculation time of 49.14%, and the solution is more efficient and stable. Secondly, setting multi-group metro operations, 3 000-piece carriage capacity and 5 min departure interval can effectively reduce the average service time by 4.6% and the total costs by 3.8%, yielding better delivery timeliness and economy. Finally, compared with the traditional logistics mode, the ground-underground closed-loop logistics distribution plan based on the existing metro network can reduce carbon emissions by 57.5%, average delivery time by 8.1% and the distribution model can provide reference for the future development of urban logistics and metro network.