摘要:智能仓储的优化一般分为货架优化和路径优化两部分：货架优化针对货物与货架两者的关系，对货物摆放位置进行优化；而路径优化主要寻找自动引导小车（automated guided vehicle，简称AGV）的最优路径.目前，大多的智能仓储优化仅对这两部分进行独立研究.在实际仓储应用中，只能以线性叠加的方式解决问题，导致问题的求解易陷入局部最优中.通过对智能仓储环节中各部分的关系进行耦合分析，提出了货位和AGV路径协同优化数学模型，将货架优化和路径规划归为一个整体；此外，提出了智能仓储协同优化框架的求解算法，包括货品相似度求解算法和改进的路径规划算法；并在以上两种算法的基础上，使用改进的遗传算法实现了货位路径协同优化.实验结果验证了所提出的智能仓储协同优化算法的有效性和稳定性.通过使用该算法，可有效提高仓储的出货效率，降低运输成本.



Abstract:The optimization of intelligent warehousing is generally divided into shelf optimization and path optimization. Shelf optimization considers the position of goods and shelves, and optimizes the placement of goods. Path optimization mainly seeks the optimal path planning for automatic guided vehicles. At present, most of the studies focus on these two scenarios independently. In the actual warehousing application, the problem can only be solved by linear superposition, which makes the solution easy to fall into the local optimum. Based on the coupling analysis of the relationship between various sections in the intelligent warehousing process, this study proposes a mathematical model of cooperative optimization of shelf and position, which combines shelf optimization and path planning as a whole. In addition, a cooperative optimization framework, including a product similarity solving algorithm and an improved path planning algorithm, is proposed. Based on the above two algorithms, an improved genetic algorithm is proposed for the cooperative optimization of shelf and path. The experimental results verify the effectiveness and stability of the intelligent warehousing cooperative optimization algorithm proposed in this study. By using this algorithm, it can improve the shipping efficiency of storage and reduce transportation costs..



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