Group Of Intelligent Hybrid Algorithm Research And Application In The Logistics Path Problem

Modern logistics is referred to as "the third profit source",more and more research attention is focused on the field, the third-party logistics is increasingly becoming the basic industry of the national economy. One of the most important part in the logistics is transportation problems, transportation costs account for more than60%of the cost of logistics. The vehicle routing problem is to reduce transportation costs by optimizing logistics distribution vehicle line, are well-known in the field of combinatorial optimization NP (Non-Deterministic Polynomial, nondeterministic polynomial) problem. Due to the complexity of the problem solving NP vehicle routing problem solving method using a variety of intelligent optimization algorithm. This paper studies the vehicle routing problem with time windows, double population algorithm based on ant colony algorithm to solve the above model, based on the mathematical model of the problem. The specific contents are as follows:First discussed the research background and significance, and summarizes the extensive literature both at home and abroad on the basis of the analysis of the research status of the vehicle routing problem and the related fields of intelligent algorithm. Then introduces the research content and innovation.Analysis the vehicle routing problem with time windows (VRPTW)systematicly, the the VRPTW definition, introduces the definition of the time window and its constraints. Then VRP, VRPTW logistics vehicle scheduling mathematical model in detail.Taking into account of the actual logistics system, the vehicle scheduling algorithms are difficult to deal with the large logistics system, in this paper, take the logistics outlets into clustering analysis, divided the outlets into a number of subsystems.This paper use a simple, convenient clustering method K-means clustering algorithm. Use SPSS Clementine software to analysis the related instance, the test results shows that the algorithm has small error rate, and achieve good clustering effect. Introduce smart algorithms used in the scheduling area, including the ant colony algorithm, particle swarm optimization, differential evolution, the advantages and disadvantages of the various algorithms. Proposed a innovative dual populations intelligent algorithms which has complementary strengths, population ant colony algorithm combined with the differential evolution (DEAS populations), population ant colony hybrid particle swarm algorithm the (PSOAS populations), in the evolutionary process allowing information to be passed in the two populations by introducing a information exchange mechanism, this improves the collaborative capabilities of the ant routing. And according to the Matlab simulation on the instance, the results show that the algorithm can find global optimal solution, and has faster convergence speed and higher stability.Finally, complete the realization of the logistics management system, the system contains transported Service management, vehicle management, scheduling eight major functional blocks of the basic information management, customer management, vehicle scheduling management, document management, financial management, driver management and system settings. The vehicle scheduling module extracts scheduling data in the basis of information management and vehicle management, call introduced populations intelligent algorithm in the background, to achieve a specific vehicle scheduling.