Research Of Genetic Algorithm And Its Application On Logistics Path Optimization

Distribution is an important link in logistics, which is joined to consumers. Thus, whether the transportation route is rational would affect the speed, cost and benefit of distribution. In order to solve the transport arrangements for this type of vehicle routing problem (VRP), all kinds of intelligent algorithms for have been used for VRP, in which the genetic algorithm is the most effective algorithm. However, there are some limitations using basic genetic algorithm to optimize the path of logistics problems such as the long string of binary code, the computing process complex, the difficulty of improved accuracy, the premature convergence of algorithm. In view of these problems, the improved genetic algorithm (IGA) was proposed in this study. Good simulation results were achieved for selecting the optimal path of logistics. The main studies are summarized as follows:Firstly, a series of improvement were proposed by analyzing the limitations of basic genetic algorithm. The use of natural number code in conjunction with adaptive crossover and mutation operators improved the ability to explore the solution space. The introduction of penalty function to deal with constraints and the corresponding operation for illegal individuals in the evolutionary process, until the individual to be the legitimate offspring of the next iteration, reduced the possibilities of premature convergence phenomenon.Secondly, models commonly used in VRP were investigated. Algorithmic methods for VRP and two general mathematical models were discussed in this study. Based on general models, a closer to the actual model of logistics system was developed.At last, the IGA were used for the developed model with time window of optimal scheduling problem of vehicles. The theoretical analysis and simulation results indicated that IGA has obvious advantages on optimal path of logistics.Overall, the use of IGA provided a higher value for logistic enterprises, which would decrease the cost, improve the level of logistics management, response customers more quickly and increase the competitiveness of the enterprise.