蚁群优化算法在物流车辆调度系统中的应用  

Application of ant colony optimization to logistics vehicle dispatching system

作　　者：李秀娟[1] 杨玥[2] 蒋金叶[1] 姜立明[1] 

机构地区：[1]辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛125105 [2]北京邮电大学国际学院,北京102209

出　　处：《计算机应用》2013年第10期2822-2826,共5页journal of Computer Applications

摘　　要：根据对蚁群算法进行的深入研究,指出了蚁群算法在解决大型非线性系统优化问题时的优越性。通过仔细分析遗传算法和粒子群算法在解决物流车辆调度系统问题的不足之处,基于蚁群算法的优点,并根据物流车辆调度系统自身的特点,对基本蚁群算法进行适当的改进,给出算法框架。并且以线性规划理论为基础,建立物流车辆系统的数学模型,给出调度目标与约束条件,用改进后的蚁群算法求解物流车辆调度系统的问题,求得最优解,根据最优解和调度准则进行实时调度。使用Java语言编写模拟程序对比基于改进粒子群算法和改进蚁群算法的调度程序。通过对比证明了所提出的改进蚁群算法解决物流车辆调度优化问题的正确性和有效性。The thorough research on ant colony algorithm points out that the ant colony algorithm has superiority in solving large nonlinear optimization problem. Through careful analysis of the deficiencies that genetic algorithm and particle swarm algorithm solve the problem of vehicle dispatching system, based on the advantage of ant colony algorithm and the own characteristics of vehicle dispatching system, the basic ant colony algorithm was improved in the paper, and the algorithm framework was created. Based on the linear programming theory, the article established mathematical model and operation objectives and constraints for vehicle dispatching system, and got the optimal solution of vehicle dispatching system problem with the improved ant colony algorithm. According to the optimal solution and the dispatching criterion real-time scheduling was achieved. The article used Java language to write a simulation program for comparing the improved particle swarm optimization algorithm and ant colony algorithm. Through the comparison, it is found a result that the improved ant colony algorithm is correct and effective to solve the vehicle dispatching optimization problem.

关 键 词：物流 蚁群优化算法 车辆调度 最佳路径 仿真验证 

分 类 号：TP315[自动化与计算机技术—计算机软件与理论]