摘要
带二维装载约束的车辆调度问题大量存在于现代物流活动中,该问题是二维装箱问题与车辆路径问题这两个经典难题融合之后的一个新问题。针对这一问题,在综合考虑客户需求、时间窗、二维装载约束、载重量以及客户满意度的基础上,建立了带二维装载约束的多目标物流配送中的车辆调度问题模型,同时,提出了一种车辆调度优化算法。该算法采用多目标蚁群优化得到Pareto最优解,在货物装载阶段采用改进的最低水平线搜索算法的二维装载策略,提高车辆装载率;在车辆路径优化阶段采用改进的信息素更新策略和客户转移概率方法,提升蚁群搜索性能。实例测试及与其他算法比较表明,该算法能有效解决模型问题,在解空间上有更好的探寻性能。
In modern logistics,there is a great amount of transportation problems called vehicle scheduling problem with two-dimensionalloading constraints,which is a new problem that combines the two classical problem of vehicle routing and bin pack. To solve the problem,we build a mathematical model of vehicle scheduling problem in multi-objective logistics distribution based on two-dimensionalloading constraints,synthesizing customer requirements,time windows,two-dimensional loading constraints,vehicle load and customersatisfaction. Meanwhile,we propose a vehicle scheduling optimization algorithm which adopts multi-objective ant colony optimization toget Pareto solutions. In the cargo loading,the two-dimensional loading strategy based on the modified lowest horizontal search algorithmis adopted to improve vehicle loading rate,and in the vehicle routing optimization,the algorithm uses the modified updating strategy ofpheromone and the method of customer transiting probability to improve the searching performance of the ant particles. The test and comparison with other algorithms show that the proposed algorithm can solve the model effectively with better performance in solution space.
作者
王增臣
周良
WANG Zeng-chen;ZHOU Liang(School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《计算机技术与发展》
2018年第10期105-110,共6页
Computer Technology and Development
基金
江苏省产学研联合创新资金项目(SBY201320423)
关键词
物流配送
车辆调度问题
PARETO最优解
多目标蚁群优化
最低水平线搜索算法
logistics distribution
vehicle scheduling problem
Pareto solution
multi-objective ant colony optimization
lowest horizontalsearch algorithm
