Multi-objective Logistics Distribution Path Optimization Based on Annealing Evolution Algorithm

Abstract Logistics distribution is a collection of interrelated organizations and facilities. There waste cost time in many links. Therefore, it particularly important to use information technology improve efficiency. Under the constraints delivery vehicle time, this paper proposes an improved genetic simulated annealing algorithm (SAGA), which combines global search ability (GA) (SA) with strong local solve routing problem windows (VRPTW). The perturbation factor introduced search, crossover method optimized obtain more efficient operators by using population information. In paper, combined actual application case, simulation experiment carried out MATLAB. experimental results show that, compared traditional algorithm, total reduced about 15%, provides suitable route planning scheme.