摘要
物流配送路径规划对于提高物流配送效率、节约配送成本具有重要意义。以物流配送路径总长度为优化目标,将其转换为经典TSP优化问题进行求解并建立了数学模型。基于该数学模型,提出改进的遗传算法,针对遗传算法的选择、交叉和变异分别提出了基于序的选择算子、基于最小代价树的交叉算子和基于随机点长度控制的变异算子。改进的遗传算法与简单遗传算法的对比仿真实验表明,所改进的遗传算法有较好的全局寻优能力,且其收敛速度快,是解决物流配送路径优化问题的有效方法。
The logistics distribution path planning is important for improving the efficiency of logistics distribution and saving logistics costs.The optimization of logistics distribution path length is converted to a classic Traveling Salesman Problem（TSP） optimization problem.The mathematical model is established.An improved genetic algorithm is put forward based on the mathematical model.Then,sequence-based selection operator,minimum cost tree-based crossover operator and random length control-based mutation operator are proposed for the selection,crossover and mutation of the genetic algorithm,respectively.The simulation results based on comparison between the improved genetic algorithm and the simple genetic algorithm show that the improved genetic algorithm has better global searching ability,fast convergence.As a result,it is an effective method to solve the logistics distribution path optimization problem.
出处
《系统工程》
CSSCI
CSCD
北大核心
2012年第8期118-122,共5页
Systems Engineering
关键词
物流配送路径优化
遗传算法
最小代价树
TSP
Path Optimization of Logistics Distribution
Genetic Algorithm
Minimum Cost Tree
Traveling Salesman Problem（TSP）
