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May 2, 2026SHILAP Revista de lepidopterologíaOpen Access

An Improved K-means Clustering Algorithm Based on EIQ Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems

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Authors

CCChuanjun ChenHFHongqiang FANJLJunjie Liu

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Overview

Randomized trial demonstrates enhanced order batching efficiency in storage systems, suggesting improved processing stability.

Key Points

  • This research aims to enhance K-means clustering for efficient order batching in shuttle-based storage/retrieval systems.
  • Proposed an enhanced K-means algorithm based on EIQ analysis.
  • Implemented IK frequency filtering for high-frequency SKUs and Pearson correlation for dimensionality reduction.
  • Used a roulette-based strategy for cluster centre initialisation and cosine distance to measure SKU similarity.
  • The improved algorithm reduced bin presentations and enhanced processing stability compared to FCFS and standard K-means.
  • Sensitivity analysis showed strong performance with feature selection thresholds of 20-25 and Pearson correlation of 0.8-0.9.
  • Greater efficiency gains were observed as the number of item-lines per order increased.

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69f593f271405d493affec68https://doi.org/10.7307/ptt.v38i4.1222
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