摘要
物流配送车辆路径问题(Vehicle Routing Problem,VRP)是一类具有广泛应用的NP-Hard问题,是解决物流配送效率的关键,传统方法寻找最优解的效率低、耗时长,往往找不到满意的解,导致物流成本过高。为了提高VRP寻优效率,降低物流运送成本,对基本遗传算法改进求解VRP问题。首先建立VRP的数学模型,然后基于贪婪随机自适应算法(Greedy Randomized Adaptive Search Procedure,GRASP)改进遗传算法的邻域搜索能力,生成遗传算法初始种群,最后利用遗传算法从GRASP生成的初始种群中找到最优解。计算结果表明,所采用的改进遗传算法可以更好的求解车辆路径问题,有效降低物流运送成本。
The vehicle routing problem whose solution is a key to improve efficiency of logistics problem is a classical NP -hard problem, and it is usually difficult for traditional methods to obtain satisfying solutions so as to high logistics costing. In this paper, in order to reduce logistics costs, the hybrid genetic algorithm was selected to solve the VRP problem. This paper established the VRP mathematic model at first. Second, the improvement using Greedy Randomized Adaptive Search Procedure （GRASP） was focused on the local search ability of basic genetic algorithm to generate the initial solution. The genetic algorithm was used to find the best solution from the initial solutions in the end. The calculation result shows that this improved genetic algorithm can solve the vehicle routing problem better than the basic one and reduce logistics costing effectively.
出处
《计算机仿真》
CSCD
北大核心
2013年第12期140-143,157,共5页
Computer Simulation
基金
国家自然科学基金(11147128)
中国科学院西部之光博士专项(XBBS201119)
新疆维吾尔自治区科技支疆项目(201291115)
关键词
车辆路径问题
遗传算法
随机贪婪自适应搜索过程
物流
邻域搜索
Vehicle routing problem
Genetic algorithm
Greedy randomized adaptive search procedure（GRASP）
Logistics
Local search
