Application Research Of The Intelligent Optimizing Technology In The Logistics System

Logistics, as a comprehensive industry in the end of the 20th century is becoming more and more important in the development of national economy and in the rising of managing level of enterprise management. To make logistics perform full function, and to make it perfect and optimized constantly, it is necessary to make logistics rationalized. Logistics systematization is the key to make logistics rationalized, so logistics must take the road to systematization. Logistics system is a complex scientific system, which includes the segments of manufacture, transport, storage and distribution. The models based on optimized problems of system are normally very complex, and most of which are NP-hard.Genetic algorithm is a random search and optimization method based on natural selection and genetic mechanism of the living beings. The combination of genetic algorithm and logistics system has good practicability and extensive application prospect. Tabu search is an expansion of the local neighborhood searching method. Tabu search is a global step by step method, which is a simulation of the human intelligent process. Tabu search and Genetic algorithm have formed relatively integrate system of arithmetic and become effective tools to solving combination optimization problems.This paper introduces the primary content of the logistics and the related problem of the logistics system. Then this paper introduces the characteristics and basic principle of genetic algorithm and tabu search, as well as their improvements. An improved crossover is proposed for natural number encoding scheme, and advanced technique like hybridization with tabu search is presented too. The improved arithmetic not only accelerates the velocity of convergence, but also avoids the precocity effectively. The improved GA and hybrid GA are applied for solving some actual problems, which include Traveling Salesman Problem, standard Delivery Problem and Flow-shop Scheduling Problem. The three application examples indicate that the improved idea is more effective and suitable to solve the combination optimization problems for modern logistics system than the traditional GA. In conclusion, the research on the GA of this paper improves the operation process of the logistics system and enhances the intellectualized level of the logistics system.