关键词: 选址路径问题, 灰狼算法, 分布估计算法, 多父代交叉, 邻域搜索

Abstract:

First, this paper proposed a mathematical model of the low-carbon location routing problem under the carbon pricing policy considering the sum of location cost, vehicle cost, and routing consumption including fuel economy. Then, it proposed a hybrid grey wolf optimizer with estimation of distribution algorithms (GWOEDA) to solve the problem by using the probability model to lead wolves to search the routes, and by using the multi-parent sequential constructive crossover and two local search operators to reinforce the global search and local search performance of the algorithm. The experimental results show that the algorithm using the probability model has a better optimization ability and the model proposed in this paper can effectively reduce the total cost under the carbon pricing policy, thereby reducing carbon emissions.

Key words: location routing problem, grey wolf optimizer, estimation of distribution algorithms, multi-parent sequential constructive crossover, neighborhood search