摘要
多车场多车型多任务的车辆调度优化是城市配送中的典型问题。针对该问题从空驶成本、运输成本和时间成本三个维度构建了一个VRP的数学模型,并采用自适应多态蚁群算法对模型加以求解。通过实例仿真,将仿真优化结果与未优化的随机结果进行了比较。结果发现优化后的成本比未优化的成本低,并且证明了对多车场多车型多任务的VRP模型进行优化非常必要。
The multi-depot and multi-task and multi-type vehicle routing problem is the typical problem in city distribution. For this issue, a VRP model is constructed based on deadheading cost, transport cost and time cost. To solve this mathematical model, a self-Adaptive and Polymorphic Ant Colony Algorithm（APACA） has been introduced. A case study is presented to compare the results based on APACA with that under stochastic condition.
出处
《计算机工程与应用》
CSCD
2013年第10期243-246,共4页
Computer Engineering and Applications
基金
国家社会科学基金项目(No.11CGL105)
北京市哲学社科规划项目(No.12JGC100)
关键词
城市配送
车辆调度
自适应多态蚁群算法
city distribution
Vehicle Routing Problem（VRP）
self-Adaptive and Polymorphic Ant Colony Algorithm（APACA）
