标题： 仓库存储与检索优化通过聚类、动态系统建模和GPU加速路由 显示英文标题

标题： Warehouse storage and retrieval optimization via clustering, dynamic systems modeling, and GPU-accelerated routing

摘要： 本文介绍了一种旨在提高产品存储和检索效率的仓库优化程序。通过在一个随时间演化的图结构中表示产品位置和订单流，我们采用无监督聚类来定义和精化紧凑的订单区域，从而有效减少了拣选距离。我们使用随机动力系统理论工具构建的动态数学模型来描述该过程，这使得即使在随机操作变化下也能对该系统的随时间行为进行原则性分析。在这个框架内进行路由时，我们实现了一个并行化的贝尔曼-福特算法，并利用GPU加速有效地评估路径段。为了应对较大路由图固有的可扩展性挑战，我们引入了一种分割策略，在保持性能的同时满足可管理的内存需求。我们的结果显示，在大规模仓库环境中，该方法在运营效率和计算可行性方面均有显著提升。 显示英文摘要

摘要： This paper introduces a warehouse optimization procedure aimed at enhancing the efficiency of product storage and retrieval. By representing product locations and order flows within a time-evolving graph structure, we employ unsupervised clustering to define and refine compact order regions, effectively reducing picking distances. We describe the procedure using a dynamic mathematical model formulated using tools from random dynamical systems theory, enabling a principled analysis of the system's behavior over time even under random operational variations. For routing within this framework, we implement a parallelized Bellman-Ford algorithm, utilizing GPU acceleration to evaluate path segments efficiently. To address scalability challenges inherent in large routing graphs, we introduce a segmentation strategy that preserves performance while maintaining tractable memory requirements. Our results demonstrate significant improvements in both operational efficiency and computational feasibility for large-scale warehouse environments.