Synthetically improved genetic algorithm on the traveling salesman problem in material transportation

The research of traveling salesman problem is important in logistics distribution. There are many stochastic disturbance factors affecting logistics distribution in real life. So the result objectivity based on distribution and recovery theory and rotation algorithm to traveling salesman problem is not good. In consideration of stochastic factors affecting on logistics distribution, using stochastic programming theory and putting forward stochastic disturbance recovery theory and synthetically improved genetic algorithm with rotation algorithm we can solve the traveling salesman problem effectively. This way can avoid the higher difference between theoretical results and the ideal ones in order to promote the manipulation of the optimal plan. Last, we take the vehicle dispatch and route choice of material transportation in Hebei province as an example, and make the simulation test to verify the affectivity of the algorithm in the meantime. The result indicates that applying the synthetically improved genetic algorithm we can improve the material transportation efficiency, and achieve high-speed mathematical operation.