Research And Application Of Logistics Enterprises Vehicle Scheduling Problem

With the rapid economic development of China, logistics enterprises(as the third party) have become a popular field in competitions and showed the obvious influence on economic activities. Whether distribution sector is reasonable or not has the direct influence on the degree of the satisfaction from the customers to the enterprises as well as their own benefits. In order to provide better service for our customers, make full use of the limited resources and promote the economic benefits, it is necessary to put forward a reasonable scheduling scheme for the logistics enterprises according to a scientific point of view. At present, the vast majority of the vehicle scheduling in logistics enterprises use the traditional method----depending on the experience of scheduling personnel. In this way, all kinds of troubles would be caused, such as waiting for the supply and shortage of goods. Meanwhile, it also has dropped the service quality of logistics enterprises. If this status quo continues to be existed, it may bring two sides a great deal of economic loss. Thus, how to optimize and establish the system of vehicle scheduling to satisfy the need of customers, which are the important issues in promoting enterprises’ profits, reducing the waste of resources and improving the service quality of enterprises. The specific contents of research are as following:First of all, the theory and application of the traditional genetic algorithm need a further study to improve the searching ability of genetic algorithm, prevent falling into the local optimal solution and improve the traditional genetic algorithm.(1) To encode the chromosome using natural number;(2) To establish a new selection formula of crossover probability and mutation probability.Secondly, the improved genetic algorithm is applied to the logistics vehicle scheduling system for establishing a logistics vehicle scheduling optimization model and genetic algorithm, and then the two algorithms need to be simulated to verify the accuracy of the improved genetic algorithm.Finally, the research achievements are applied to the actual logistics vehicle scheduling management system to achieve the shortest path of transport vehicles and reduce transport costs of relative reduction target. The research results have a certain practical application value.