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一种基于AGVs的智能仓储批量分拣模型及方法
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Abstract:
本文研究了一种基于AGVs的智能仓储批量分拣模型及两阶段改进批量分拣优化方法。分析了受能量约束的AGV行驶路径问题，考虑以最小化AGV行驶距离为优化目标，构建多AGVs批量分拣数学模型。在迭代改进分拣优化的第一阶段利用待分拣订单位置，订单特征生成高质量初始解。第二阶段考虑使用智能优化算法，分别利用模拟退火算法和遗传算法对第一阶段生成的优质初始解进一步改进，并对所适用的智能仓储规模进行分析与验证。本文构建的分拣模型充分考虑了货架布局的多样性、订单构成的通用性、以及AGV行驶路径的规范性，更加适用于实际智能仓储分拣需求。提出的方法对于具有不同规模、不同分布的货架及订单都能够高效地进行分拣，在提升AGV利用率的同时降低智能仓储总能耗，为实际智能仓储应用提供可适配的柔性支撑。
This study investigates an intelligent warehouse batch picking model based on AGVs, along with a two-stage enhanced batch picking optimization method. The AGV travel path problem under energy constraints is analyzed. With the objective of minimizing AGV travel distance, a mathematical model for batch picking with multiple AGVs is constructed. In the first stage of iterative sorting optimization, high-quality initial solutions are generated using the positions and characteristics of orders. In the second stage, intelligent optimization algorithms, simulated annealing algorithm and genetic algorithm, are applied to further improve the high-quality initial solutions obtained in the first stage. Analysis and verification of applicable intelligent warehousing scales are also conducted. The sorting model developed in this study takes into account the diversity of shelf layouts, the generality of order compositions, and the standardization of AGV travel paths, making it more suitable for practical intelligent warehousing sorting requirements. The proposed method efficiently handles sorting for shelves and orders with varying scales and distributions. It enhances AGV utilization while reducing the overall energy consumption of intelligent warehousing, thus providing adaptable and flexible support for practical intelligent warehousing applications.
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