Hybrid Intelligent Optimization Algorithm And Its Application In Vehicle Routing Problem

Nowadays, logistics has been recognized as the third important source to create greater profits besides reducing material consumption and improving labor productivity. It is also an important way to reduce production and management costs, as well as improve product market competition. The relevant data statistics show that modern logistics costs account for about30%to50%of the total cost of the enterprise management. Logistic distribution is an important part of the logistics system, it is the last part of the customer services provided by the enterprise, so its position is very prominent, and especially the vehicle routing problem on the distribution sectors has the greatest impact. Our country’s logistics and distribution system is very backward, serious vehicle no-load, high distribution costs, which lead to service quality is always unsatisfactory. Therefore, how to use scientific methods to solve vehicle routing problem, to obtain reasonable vehicle delivery path, which is extremely necessary to improve the quality of service, reduce inventory, lower operating costs and improve the market competitiveness of their products.Modern intelligent optimization algorithms including tabu search algorithm, simulated annealing algorithm, genetic algorithm, ant colony algorithm, neural network algorithms, and the existence of these algorithms provides a broad ideas and options to solve the Vehicle Routing Problem. Among these algorithms, the application of Tabu search algorithm, simulated annealing algorithm and genetic algorithm in the vehicle route optimization problem solving were not used for a long time, although there has been a lot of research achievements, their potential remains to be further excavated. Ant colony algorithm’s global search performance is good, but its search efficiency is low, also its local search ability is not strong. On the other hand, the performance of tabu search algorithm is exactly the opposite, but the application of Tabu Search Algorithm has strong dependence on the initial solution. Therefore it is necessary to propose a new hybrid intelligent optimization algorithm, which would learn from each other for better solution.In order to solving the shortest path of enterprise logistics instance, based on the above ideological and with deeply studying the vehicle routing problem,a new hybrid intelligent optimization algorithm is proposed,which achieves the Conversion of logistics path optimization problem into the constrained problem which would solved by hybrid intelligent optimization algorithm. In this paper, by using a flexible universal coding strategy, a hybrid intelligent optimization algorithm based on improved ant colony algorithm is proposed. The simulation proved the new hybrid algorithm has a convenient, fast and efficient feature, also its experimental datas show that the. new hybrid algorithm is better than a single algorithm. Above description is the difficulty and innovation.Current logistics occupies a pivotal position in modern enterprises, now competition between enterprises has been evolved into the competition between enterprise logistics. The algorithm can quickly and efficiently get the shortest vehicles delivery path, which would maximize the benefits of logistics and distribution. Therefore, this article has important theoretical significance and great value to solve the vehicle routing problem.