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Genetic Algorithms for Vehicle Routing Problem

HUANG Ming,LIN Guang-zhi,LIANG Xu,WANG De-guang(Softwate Institute,Dalian Jiaotong University,Dalian 116028,China)  
Based on the vehicle routing problem in-depth analysis,and direct against the most important two factors of population diversity and pressure choosing,the main working is improved the main factors cross operator and mutation operator.And compared with the genetic algorithm,results of numerical tests show that:on one aspect of the vehicle routing:the algorithm has higher rate of convergence,more superior solution,and more stable of calculation result;on the anther aspect of the vehicle routing:the optimal delivery route is four in improved algorithm,and the four lines no cross and loops completely,but also simultaneously satisfy the rate of vehicles loaded with restrictions,and general genetic algorithm has been five distribution route,the minimum load is only 3.1,and should not reach the vehicle loaded with the corresponding rate.Therefore,an improved genetic algorithm is obviously superior to the traditional genetic algorithm.
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