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

2025-04-29

AI Summary

This paper addresses core challenges in large-scale warehouses—low storage/retrieval efficiency, excessive picking distances, and computationally intractable routing. Methodologically, it proposes a co-optimization framework integrating time-varying graph modeling and GPU acceleration. Specifically, it constructs a dynamic time-varying graph linking storage locations and orders; applies unsupervised clustering to partition compact order zones, thereby minimizing travel distance; and introduces a novel routing mechanism that unifies stochastic dynamical systems modeling with a parallelized Bellman–Ford algorithm, complemented by a performance-preserving segmentation strategy to overcome memory bottlenecks in large-scale graph computation. Experiments demonstrate that the framework significantly reduces average picking distance, enables real-time and scalable route planning, and substantially improves operational efficiency and computational feasibility. It establishes a new paradigm for intelligent warehousing that balances theoretical rigor with engineering practicality.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Optimizing warehouse storage and retrieval efficiency
• Reducing picking distances via clustering and dynamic modeling
• Scalable GPU-accelerated routing for large warehouses

Innovation

• Unsupervised clustering for compact order regions
• Dynamic model with random dynamical systems theory
• GPU-accelerated parallel Bellman-Ford algorithm