Research of an Improved Genetic Algorithm in Logistics Freight Vehicle Routing Problem

Abstract

Logistics traffic scheduling is a key job for logistics activity, but there are some weak points, e.g. local optimum, premature convergence and slow convergence while we employ the traditional genetic algorithm to analyze that problem. Therefore, we need to improve the genetic algorithm to solve the vehicle routing problem. This paper builds up a mathematical model for the traffic scheduling problems and advance an improved gene arithmetic. We simulate that model and the results from the simulation show the improvements are advanced and practical, and the model can improve the efficiency to find the optimal distribution path and save the transportation costs.