Single-depot Vehicle Routing Problem Based On Ant Colony Optimization

With economic development, in the future market competition) logistics as "the third profit source " will play a pivotal role. Meanwhile, the modern intelligent logistics system is one of the hotspots of today’s research. Logistics and distribution is an important part of the logistics system, it is the important way the logistics enterprises to improve efficiency, reduce costs, improve customer satisfaction and Enhances the enterprise competitive power that to optimize logistics distribution. Therefore, the study vehicle scheduling in logistics and distribution needs, the establishment of mathematical model of vehicle scheduling, and propose effective, the general applicability of the vehicle scheduling problem with a certain intelligence optimization methods, and then developed a vehicle scheduling system has important theoretical significance and practical value.This paper defined the Vehicle Routing Problems, discussed the scheduling requirements and constraints. established the vehicle scheduling model of a constrained environment, in theory, on the distribution vehicle scheduling problem is studied and its various types of commonly used Solution briefly compared, theoretically conducted the research the vehicles scheduling problem and made the simple comparison. Second selected ant colony algorithm to solve the vehicle scheduling problem, introduced the emergence, development, the research present situationant of the ant colony algorithm, analyzed the principle of ant colony algorithm and realization of process, and the algorithm in vehicle scheduling problem of application,made a simple analysis of the basic ant colony algorithm for the advantages and disadvantages. proposed the corrective method to solves search initial period convergence rate slow shortcoming and solves easily to restrain in the partial optimal solution flaw.In the improved algorithm, using the characteristic that the ant colony algorithm is easily combined with other heuristic algorithms, ant colony algorithm and genetic algorithm combined.Ultimately the effective improvement of the algorithm to solve the problem of basic ant colony algorithm for the defects, the improved algorithm guarantee the validity of practical problems. To further verify the feasibility and applicability of the improved algorithm, this process through the development of vehicle scheduling problems on the simulation, to problem solving, and achieved satisfactory results. The experimental result indicated that the improved ant colony algorithm performance is fine, obtained in a short time vehicle scheduling problem with satisfaction the optimal solution.