基于遗传-集束搜索的立体车库堆垛机运行路径优化

针对巷道堆垛式立体车库堆垛机在存取车过程中运行路径较长的问题,本文提出一种基于遗传算法(Genetic Algorithm,GA)优化的集束搜索用来优化堆垛机运行路径。以缩短存取车过程中顾客平均等待时间和堆垛机运行距离为目标,通过对实际运行立体车库的考察和数据提取,建立立体车库堆垛机运行路径的数学模型,同时比较GA-集束搜索混合算法与集束搜索算法的实验仿真数据,仿真结果表明：GA-集束搜索混合算法具有较好的收敛性,能够有效地缩短顾客平均等待时间和堆垛机运行距离,提升立体车库运行效率。

Research on Operation Path Optimization of Stereo Garage Stacker Based on GA-Beam Search Hybrid Algorithm

Aiming at the problem that the roadway stacking stereo garage stacker has a long running path during the accessing vehicles process, this paper proposes a beam search algorithm base on genetic algorithm (GA) to optimize the stacker running path. In order to shorten the average waiting time of customers and the running distance of the stacker during the process of accessing the vehicle, the mathematical model of the running path of the stacker in the three-dimensional garage is established through the investigation and data extraction of the actual running three-dimensional garage. Comparison of experimental simulation data under GA- Beam search hybrid algorithm and beam search algorithm. The simulation results show that the GA-Beam search hybrid algorithm has better convergence, which can effectively shorten the average waiting time of customers and the running distance of stacker, and improve the running efficiency of stereo garage.