Research Of Multiple Vehicles Routing Problem With Stochastic Demand Based On Chaotic Neural Network

With the development of economy, distribution systems have become increasingly complex, and are more and more important for economic influence, so researchers pay attention to them at home and abroad. Vehicle routing problem is an important content in supply chain field. Selecting appropriate vehicle routing method can accelerate for custom as thing flows the satisfactory degree of link and reduce service business running cost. The existing research of multiple vehicles routing problem (VRP) with stochastic demand is not consummate in domestic and abroad. The scholar on research of multiple vehicles routing problem with stochastic demand begin to pay more attention to seek more effect heuristic.Based on the description of the MVRPSD, this paper proposes a calculation method of path failure probability with stochastic demand. According to the method, The problem of certain number of service failure can be solved, with multiple service failures. A deterministic cost function with stochastic demand which is constructed, making it possible to solve random problems. In addition, this paper indicates a kind of annealing chaotic neural network algorithm to multiple vehicles routing problem with stochastic demand, targeting to the shortest total distance and the least number of vehicles, while standard Hopfield neural network easyly falls into local optimum. In this algorithm, chao lasts long enough to improve the search performance of crude, and also convergence rate is larger while chaotic dynamics weakens. According to the experimental results, vehicle routing is better using this algorithm, and the algorithm is a effective method for solving multiple vehicles routing problem with stochastic demand. It is a way to solve some practical problems of logistic vehicle delivery to some extent by the research, as well as improving the efficiency of vehicle delivery services and the level of profits. It also provides a theoretical and practical reference for other industries with the process optimization.