Warehouse storage and retrieval optimization via clustering, dynamical systems modeling, and GPU-accelerated routing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908601072746496 |
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| author | Bengtsson, Magnus Wittsten, Jens Waidringer, Jonas |
| author_facet | Bengtsson, Magnus Wittsten, Jens Waidringer, Jonas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Warehouse storage and retrieval optimization via clustering, dynamical systems modeling, and GPU-accelerated routing Bengtsson, Magnus Wittsten, Jens Waidringer, Jonas Optimization and Control Data Structures and Algorithms Dynamical Systems 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. |
| title | Warehouse storage and retrieval optimization via clustering, dynamical systems modeling, and GPU-accelerated routing |
| topic | Optimization and Control Data Structures and Algorithms Dynamical Systems |
| url | https://arxiv.org/abs/2504.20655 |