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

摘要：

针对碳定价背景下的低碳选址路径问题（Low-Carbon Location Routing Problem，LCLRP），首先构建了一种考虑油耗和碳排放成本，并以最小化设施选址成本、车辆启用成本以及运输成本为目标的选址-路径模型；其次，根据模型的特征，设计了一种分布估计灰狼算法（Grey Wolf Optimizer with Estimation of Distribution Algorithms，GWOEDA）对其进行求解。算法利用概率模型引导灰狼，并利用多父代交叉和两种邻域搜索算子增强了算法的全局搜索与局部搜索性能。算例分析结果表明：加入概率模型学习能力的灰狼算法在选址路径问题上有更好的寻优能力，并且在碳定价背景下，所构建的模型可以有效地降低总成本和碳排放量。

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