Optimization Model and Algorithm of Cigarette Distribution Route Based on Cluster Analysis

Abstract

Cigarette distribution is an important part of the tobacco logistics supply chain, and it will affect the distribution cost, efficiency, and service quality. In this paper，we choose a two-stage optimization method to analyze the cigarette distribution route across the Administrative Region in China. First, we use a K-means clustering algorithm to generate an initial center. Then, we optimize the clustering region with the vehicle load and workload as constraints. Finally, we establish the cigarette distribution route optimization model by taking the lowest distribution cost as the objective function. In the model calculation, we not only select the adaptive genetic algorithm as the solution algorithm but also use the scanning algorithm to generate the initial population ,design adaptive crossover and mutation probability, and also design inverse operator to correct the error, and we wish to improve the solution performance of the algorithm by this way.In addition, taking the cigarette distribution of Chongqing Tobacco Logistics as an example, this paper analyzes the number of vehicles, average loading rate, and total distribution distance and distribution cost, and finds that the cross-regional joint distribution route is better than independent distribution. And we also find that using clustering analysis and the genetic algorithm could reduce the amount of computation and solve the problem effectively.