摘要
在城市快递配送复杂性、经济性、时效性、服务性以及不确定性的特征下,本文研究双重时限下快递员送货问题。在单次作业软时间窗、快递员一天总作业时间及装载量约束的条件下,计算所属有向路网内完成派送任务所需最少快递人员数,同时得出他们的任务分配与路径选择。首先,采用CARP问题定义城市快递派送,根据双层装箱问题建立0-1整数规划模型,第一层装箱定义为快递员在进行单次配送任务时,其装载量和单次配送时间满足小箱"容量"限制,第二层装箱定义为快递员一天中完成所属若干个配送任务的总时间满足大箱"容量"限制。其次,由于双重时限的特性,快递员单次配送时间灵活多变,本文通过将各需求弧转化为点,赋予其时间属性及装载量属性,将原问题转化为VRP问题,采用lingo软件编程求得其精确解的同时也有效解决了CARP问题中子回路消除的问题。最后,构造求解算例,验证该模型的可行性。
Urban express delivery is a problem with the properties of the complexity economy, timeliness, service and uncertainty. This paper focuses on the delivery problem under double restrictions of soft time window and maximum working time in a day. Under the constrains of single operation soft window,courier total work time and the loading capacity,the needed courier number to finish the deliverys in a urban road network,the delivery task distribution and the path selection were calculated. First, a CARP and 0-1 integer programming model based on the double packing problem were chosen to define the urban delivering problem. The first packing problem was defined as when a courier worked for a single distribution tasks, its loading capacity and single delivery time had to meet the small box＇s ＂capacity＂ limits; the second packing problem was defined as that the total time, a courier finished his whole single distribution tasks, had to meet the big box＇s ＂capacity＂ limits. Then, because of the double time restrictions, the courier＇s single delivery time was flexible. By transforming the demanding arcs into the demanding points and giving them the time and capacity attributes, the CARP was transformed into a VRP. Lingo software was chosen to get the accurate solution, meanwhile, the sub-circuit problem of the CARP was solved. At last, an example was used to prove the model＇s feasibility.
出处
《交通运输工程与信息学报》
2016年第2期101-109,共9页
Journal of Transportation Engineering and Information
基金
中央高校基本科研业务费专项资金(SWJTU09ZT18)
四川省科技支撑计划项目(2014GZ0019-1)
关键词
快递派送
双层装箱问题
图转换法
子回路消除
Courier delivering
double packing problem
path planning
graph transformation
sub circuit eliminating
