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In this study, we propose LDAS as a method to optimize storage locations in automated warehouses. LDAS combines Latent Dirichlet Allocation (LDA) and Simulated Annealing (SA). We apply LDA to capture item co-occurrence patterns in customer orders. Using a metric derived from the LDA, we develop a methodology to explore storage location in the warehouse by SA. The experimental results using a real data demonstrate that the proposed method achieves higher throughput and greater stability compared to existing methods.

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LDAS: Proposal for Semi-optimal Storage Locations Using LDA and SA

  • 会议论文

  • 发表日期：

    2026年

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  • Tatsuto Ito,
  • Naoki Hattori,
  • Hiroki Skaji,
  • Itsuki Noda

摘要

In this study, we propose LDAS as a method to optimize storage locations in automated warehouses. LDAS combines Latent Dirichlet Allocation (LDA) and Simulated Annealing (SA). We apply LDA to capture item co-occurrence patterns in customer orders. Using a metric derived from the LDA, we develop a methodology to explore storage location in the warehouse by SA. The experimental results using a real data demonstrate that the proposed method achieves higher throughput and greater stability compared to existing methods.