Research On The Optimization Of Modern Logistics Distribution Routing Problem

Distribution is an operation linking directly with consumers, and takes account for considerable proportion in variable costs in logistics. The planning of vehicle routing in distribution will have significant effect on the efficiency, cost and benefit, especially in distributing for multi-consumers. Hence, vehicle routing problem (VRP) has become focus of many scholars to study.This research exploits physical distribution vehicle routing problem. The paper consists of introduction, three chapters, and conclusions. The introduction gives us a brief picture of the importance of the logistics science research, and the main reason why people are paying more attention to it, then introduces the idea that the study on optimizing the distribution route of logistics transportation is very important. Chapter two provides the optimization of distribution routing problem after presenting the present situation of the study on logistics at home and abroad. Chapter three focused on the Saving Method, by case studying, the advantages and disadvantages are proposed. What's more, the effectiveness of Sweeping Method and Tabu Search algorithms when used to optimize distribution routing problem are analyzed. Chapter four proposes the Improved Genetic Algorithm (IGA) for the solution of this problem. Several measures are taken: (1) Motivates schemata theorem of Natural Number Coding, which provides the theoretical basis for further research. (2) Selects reasonable population size by analyzing the relationship between the number of customers and the population size. (3) Using the idea of Generation Gap to optimize population quality. (4) Provides Analogy Partally Matched Crossover (PMX) operator to improve the performance of chromosome and decrease destroy. (5) Introduces Inversion Operator to improve evolution quality of population and fasten solution to optimal results. (6) The computational results demonstrate effectiveness of IGA used to solve distribution routing problem is proved, which also illustrates that IGA has great robustness and effectively solve the "Premature Convergence " problem. Conclusions contain a summary and suggestions for further research.