摘要： 为解决多种货物、多卸载点、多车承运中的车载装箱问题,建立一种基于装箱约束的多卸载点车载装箱模型。根据寻找最优装箱顺序和寻找最优路径的相似性,设计求解多卸载点车载装箱问题的多信息素蚁群算法,使用2类邻域缩减蚂蚁寻路的范围,通过权重的变化控制局部
信息素和全局信息素的比重,从而提高算法的收敛速度并防止陷入局部最优。实验结果表明,该算法在体积利用率、重量利用率和所用车辆数量方面都优于贪心算法,在收敛速度方面快于单信息素蚁群算法。
关键词:
车载装箱问题,
蚁群算法,
多信息素,
单信息素,
邻域
Abstract: For a variety of goods,multi-unloading point,vehicle-mounted bin packing problem with multi-carrier,a vehicle-mounted bin packing model of multi-unloading point based on packing constraints is proposed.According to finding the optimal packing order and the optimal path similarity,a multi-pheromone of ant colony algorithm for solving multi-unloading point of vehicle-mounted bin packing problem is designed.This algorithm improves the convergence speed by reducing the scale of the ant routing based on two kinds of neighborhood,and improves the convergence speed and avoids local optimum by using the weight to control the local pheromone and global pheromone proportion.Experimental results show that
this algorithm is better than the greedy algorithm in the volume utilization rate,capacity utilization rate and the number of vehicles,and is better than single pheromone of ant colony algorithm in the speed of convergence.
Key words:
vehicle-mounted bin packing problem,
ant colony algorithm,
multi-pheromone,
single pheromone,
neighborhood
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