基于遗传禁忌搜索算法的自动化立体仓库出入库路径优化研究

Optimization of the Loading/Unloading of an Automated Warehouse Based on Genetic Algorithm and Tabu Search

针对自动化立体仓库出入库路径优化问题,在分析了遗传算法和禁忌搜索各自优缺点的基础上,通过把遗传算法作为搜索的主框架,禁忌搜索作为遗传算法的变异算子,将遗传算法和禁忌搜索结合起来,提出了遗传禁忌混合算法来求解仓库出入库路径优化问题的策略。对比试验结果表明,该算法有效地克服了基本遗传算法搜索能力差、收敛慢的弱点,提高了自动化立体仓库出入库的效率,具有明显的优越性。

To solve the path optimization problems of an automated warehouse,based on analyzing the advantages and disadvantages of traditional genetic algorithm（GA） and tabu search（TS）,a hybrid genetic algorithm and tabu search（GATS） is proposed to solve the path optimization problems of the automated warehouse.The main frame of the algorithm is provided by genetic algorithm,and tabu search is used as a mutation operator of the genetic algorithm in this hybrid algorithm.The contrastive results show that the proposed GATS algorithm effectively overcomes the weak search ability and the slow convergence existing in traditional GA.It improves the automated storage efficiency of the automated warehouse and has obvious superiority.