基于分段遗传编码的配送路径优化

为减小物流配送中车辆路径优化这一NP难题的组合规模，增大其搜索空间及寻优效率，提出了一种分段编码方法，编码中的各段表示相应车辆路径的需求城市集合。以非完全连通配送网络为研究对象，基于分段遗传编码，构造了车辆路径优化问题的遗传算法，通过对编码各段超载、包容等的判断，得出一个关于需求城市的最优划分。最后利用改进的迪杰斯特拉算法，根据最优化分中的需求城市，求取最优的配送路径集。计算示例证明了该方法的有效性。

Logistic Route Optimization Based on Sectional Genetic Coding

In order to reduce the combination scale, enlarge the searching space and improve the optimizing efficiency of the NP-Hard problem of vehicle route optimization, a new sectional coding was presented in this paper. Each section in the code represented the set of demand cities of a vehicle route. Then the genetic algorithm of the problem was structured, which is based on the sectional coding for a non-entire connection network. Through the judgment of overloading and containment of every section in the code, an optimal partition of demand cities could be obtained, Finally, the optimal route set was achieved through the improved Dijkstra algorithm according to the demand cities in the partition, and the validity of the above mentioned approach was proved by a case study.