摘要
为提高车辆配送效率,节约配送成本,建立了以配送路径和成本综合最优为目标的车辆配送路径问题数学模型.设计并实现了一种智能混合算法,首先利用具有自适应交叉率和变异率的改进遗传算法生成全局较优解,再将较优解转换为初始信息素进行蚁群算法,并结合2-opt算法对解进一步迭代优化,最终获得了车辆最优配送路径.实验结果表明,该算法优化后的目标值比蚁群算法减少了15.0%,比遗传算法减少了10.4%,验证了该算法的有效性和优越性.
In order to improve the efficiency and reduce the cost of vehicle delivery,a VRF mathematic model on optimizing vehicle routing and cost was established.An intelligent hybrid algorithm was proposed.Hybrid genetic algorithm which combined with self-adaptive crossover rate and mutation rate was used in the algorithm to conduct the global better solution.Then the better solution was taken as the initial solution of the ant colony algorithm and the stage solution was optimized by 2-opt algorithm to obtain the best vehicle routing.The experimental result showed that the objective value based on hybrid algorithm was 15.0%less than ant colony algorithm and 10.4%less than genetic algorithm,so the efficiency and superiority of the intelligent hybrid algorithm were proved.
出处
《延边大学学报（自然科学版）》
CAS
2015年第3期261-266,共6页
Journal of Yanbian University（Natural Science Edition）
基金
泉州市科技局社会发展计划项目(2012Z132)
黎明职业大学校科研团队项目(LMTDD2014108)
关键词
车辆配送路径问题
智能混合算法
遗传算法
蚁群算法
2-opt算法
vehicle routing problem
intelligent hybrid algorithm
ant colony algorithm
genetic algorithm
2-opt algorithm
