The TSP solution for the supermarket chains supply route based on "Ant Colony - Particle Swarm" algorithm

There are small amount, multi-varieties and short shelf life of the characteristics of fresh agricultural and fishery products in the distribution between supermarket chains distribution centers and stores. The empty loading rate is high in the one-to-one line of distribution, which causes an empty return phenomenon, resulting in higher distribution costs. To reduce the costs of supply logistics in same city, the TSP solution is employed to optimize the problem to make the effect that one route serves numerous demand points. Taking the supply route of a supermarket in Jinan City as an example, the Simulated Annealing, Genetic algorithm and Ant Colony algorithm are utilized to calculate the optimal routes at 10 times. The advantages and the disadvantages of each algorithm for this case are compared. Based on the above, a joint algorithm "Ant Colony-Particle Swarm" algorithm is designed. Through the solution of the supply route in supermarket chains in same city, the joint algorithm "Ant Colony - Particle Swarm" algorithm proved to be having a high convergence speed and good practical value for the TSP problem.