Abstract: Aiming at the problems of the dense distribution and the dynamic changes of cargo spaces in the location allocation of four-way shuttle storage and retrieval system, a multi-objective cargo space allocation method aiming at improving warehouse work efficiency and equipment stability is established, and a new method is proposed. An improved shuffled frog leading algorithm is proposed to simulate and optimize the model. First of all, according to the distribution characteristics of cargo spaces in intensive warehousing, a strategy of cargo space and cargo lanes is proposed, and a cargo space allocation principle for goods stored in cargo lanes by category is established. Then, a mathematical model of cargo space allocation is established with the goal of shortest path, balanced distribution of goods and lower center of shelves gravity. Finally, an improved shuffled frog leading algorithm is proposed to solve the model by using dynamic adaptive crossover to improve the local search strategy of the standard shuffled frog leading algorithm. The simulation results show that the cargo space allocation model is reasonable, and compared with the standard genetic algorithm and the standard shuffled frog leading algorithm, the improved shuffled frog leading algorithm has faster convergence speed, more reasonable optimization of cargo space, and can effectively solve the problem of location allocation of four-way shuttle storage and retrieval system.

Key words: dense storage, location allocation, shuffles frog leaping algorithm, genetic algorithm

摘要： 针对四向穿梭车式密集仓储货位分配中存在的货位密集分布、货位动态变化等问题，建立了以提高仓库工作效率、设备稳定性为目标的多目标货位分配方法，提出了一种改进的混合蛙跳算法求解。针对密集仓储的货位分布特点，提出货位分货道策略，建立货物按类别分货道存放的货位分配原则。以路径最短、货物分布均衡及货架重心稳定均衡为目标建立货位分配数学模型。使用动态自适应交叉改进标准混合蛙跳算法的局部搜索策略，提出了一种改进的混合蛙跳算法对模型进行求解。通过实例仿真验证结果表明：货位分配模型合理，并且与标准的遗传算法和混合蛙跳算法相比，改进后的蛙跳算法收敛速度更快，对货位的优化更加合理，可以有效解决四向穿梭车式密集仓储货位分配问题。

关键词: 密集仓储, 货位分配, 混合蛙跳算法, 遗传算法