Abstract: Aiming at the problems of high no-load rate and high expenditure cost in urban logistics distribution,considering the increasingly congested traffic conditions,a path optimization mathematical model was constructed to minimize the total expenditure of individual customers,e-commerce enterprise customers and the transportation department based on the analysis of E-Commerce orders served by the transportation department and the collection and delivery of orders in the same city.To solve this problem,considering the shortcomings of genetic algorithm,such as relying on the initial solution and easy to fall into the local optimal solution,a new improved genetic algorithm was designed by using the insertion heuristic algorithm to improve the initial population and introducing the improved elite retention strategy and queen bee evolutionary crossover strategy to improve the search ability of genetic algorithm.Taking the urban distribution network optimization of an e-commerce company in Hunan Province as the data source,the improved genetic algorithm was used to compare and analyze the existing related algorithms,and the effectiveness of the model and algorithm was verified.The experimental results showed that the co- distribution mode could not only effectively improve customer satisfaction,but also reduce the no-load rate of distribution vehicles and the total expenses of three parties.

Key words: urban joint delivery, simultaneous pickup and delivery, route optimization, improved genetic algorithm

摘要： 针对城市物流配送存在的空载率高、支出费用高等问题,考虑到日益拥堵的交通路况,在分析运输部门对电商订单进行送货和对同城订单进行取送货的基础上,构建了以个人客户、电商企业客户和运输部门总支出费用最小化为目标的路径优化数学模型。为求解该问题,考虑到遗传算法存在依赖初始解、易陷入局部最优解等缺点,运用插入启发式算法改良初始种群的同时,引进改良精英保留策略和蜂王进化交叉策略提升遗传算法的搜索能力,设计了一种新的改进遗传算法。以湖南省某电商公司的城市配送网络优化为数据来源,运用改进遗传算法与现有相关算法进行了对比分析,验证了模型和算法的有效性。实验结果表明,城市物流共同配送方式不仅能有效提升客户的满意度,还能降低配送车辆的空载率和三方主体支出的总费用。

关键词: 城市共同配送, 同时取送货, 路径优化, 改进遗传算法