基于混合遗传算法的物流车辆调度优化

Vehicle Schedule Optimization of Logistics Based on Combinational Genetic Algorithm

针对遗传算法在求解车辆调度问题时容易出现早熟现象,导致求解精度不高的问题,本文用混合算法构建了物流配送总成本最小的目标函数。首先,定义了车辆调度问题的数学模型,在此基础上提出了一种遗传算法中对交叉和变异概率的自适应调整的方法。其次,通过局部搜索算法求得初始解,采用遗传算法初始解优化,并且在配送时刻改变以后,利用TS算法搜索最优解迅速的特点改进配送方案,最终求得配送时刻不断变化下的车辆调度方案。最后通过算例分析,得到本文提出的算法与单一局部搜索算法和单一TS算法相比,在求解精度、求解时间方面都具有更大的优越性。

Aiming at genetic algorithm in vehicle scheduling problem, which is prone to premature phenomenon and lead to the prob- lem is not high. With a combinational algorithm, we build the objective function is minimum under total cost of logistics distribu- tion. First, the mathematical model of vehicle scheduling problem is defined, and based on this proposed a genetic algorithm for ad- justing the adaptive crossover and mutation probability of method. Second, the initial solution are obtained by local search algo- rithm, and optimized by the genetic algorithm. The scheduling will be improved by the advantage of the characteristic of the Tabu search algorithm as soon as the delivery time is changed. And the moment distribution is obtained under the changing of vehicle scheduling scheme. Finally, an example is proposed to show that the single local search algorithm and the single Tabu search algo- rithm compared with this combinational algorithm in the aspects of precision and the time of solving, this method has more superior- ity.