Abstract: To meet the needs of metal processing and equipment manufacturing enterprises for the safety and efficiency of large and heavy-duty automatic warehouses,a multi-objective optimization model for slotting problems was established,which considered the requirements of warehouse entry and exit efficiency,shelf stability,load balance of stackers.For such a model,a two-stage hybrid algorithm that integrated the advantages of Genetic Algorithm (GA) and Late Acceptance Hill-Climbing named GA-LAHC was proposed.Meanwhile,the crossover operator of storage-position greed was designed to enhance the information perception ability of GA and accelerate the convergence speed.The effectiveness and parametric sensitivity of the algorithm were analyzed by simulation experiments.Experimental results showed that the proposed algorithm performed better than PSO,GA,and LAHC in solution stability and accuracy.

Key words: automated warehouse optimization, large-scale slotting optimization, genetic algorithms, late acceptance hill-climbing algorithm, two-stage hybrid algorithm

摘要： 为满足金属加工及设备制造企业对大型重载立体仓库安全、高效运作需求,建立了考虑仓库出入库效率、货架稳定性和堆垛机负载均衡要求的货位分配多目标优化模型。针对该模型提出了一种两阶段混合算法(GA-LAHC),该算法集成了遗传算法(GA)和延迟接受爬山算法(LAHC)的优势。同时,设计了货位贪婪交叉算子,以增强遗传算法的信息感知能力,加快算法收敛速度。通过仿真实验对算法的有效性和参数敏感性进行了分析,结果表明所提出的GA-LAHC算法在求解稳定性和求解精度上优于粒子群算法、GA及LAHC。

关键词: 立体仓库优化, 大规模货位分配, 遗传算法, 延迟接受爬山算法, 两阶段混合算法