Improved Hopfield Neural Network's Applied Research In Vehicles Dispatch

This thesis have researched on the scheduling problem of the vehicles of the logistics distribution center, proposed the blending hopfield neural network algorithm based on simulated annealing algorithm, solved vehicles routing problem with width time windows and the capacity to restrain. The simulation annealing algorithm and the hopfield neural network algorithm has some flaws ,the slow convergence rate, easy to fall into partially most excellent, causes the system not to be satisfying. This article proposed the blending network algorithm, this algorithm unifies respective merit to overcome each other's insufficiency through blending the hopfield neural network and the simulation annealing algorithm, using certain probability receive bad solution mechanism in the simulation annealing algorithm to the hopfield neural network algorithm, overcomes the flaws of falling into partially most excellent. Simultaneously has made the improvement to the traditional network and the simulation annealing algorithm, obtained one kind of fast and highly effective restraining algorithm finally. Finally using the mix algorithm to sovle actual distribution problem in Rain Run group, has made the good progress.