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

Chuanjun Chen, Hongqiang FAN, Junjie Liu, Shun Li
April 28, 2026
Published Date

Research Abstract & Technology Focus

Shuttle-based storage/retrieval systems (SBS/RS) require efficient order batching to optimise split-case picking. Original K-means clustering, which groups orders based on overlapping SKUs to minimise bin presentations, struggles with high-dimensional, sparse pharmaceutical data due to computational inefficiency, unsuitable distance metrics and unstable initialisation. We propose an enhanced K-means algorithm based on EIQ analysis. High-frequency SKUs are selected using IK frequency filtering, while Pearson correlation is applied to remove redundant features and reduce dimensionality. Cluster centre initialisation is improved using a roulette-based strategy, and cosine distance replaces Euclidean distance to better capture SKU similarity. Case studies using real data from Company A show that the proposed method outperforms both first-come-first-serve (FCFS) and standard K-means in reducing bin presentations and enhancing processing stability. The algorithm remains robust regardless of SKU popularity shifts. Sensitivity analysis confirms strong performance within appropriate thresholds for feature selection (n: 20–25) and correlation filtering (Pearson correlation: 0.8–0.9). Furthermore, as the number of item-lines per order increases, the improved algorithm yields greater efficiency gains. This algorithm can also be well applied to other industries.
Cluster analysis Euclidean distance Bin Algorithm Computer science Sensitivity (control systems)
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Frequently Asked Questions (FAQ)

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What is the core focus of the research titled 'An Improved K-means Clustering Algorithm Based on EIQ Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems'?

This literature focuses on: Shuttle-based storage/retrieval systems (SBS/RS) require efficient order batching to optimise split-case picking. Original K-means clustering, which groups orders based on overlapping SKUs to minimise bin presentations, struggles with high-dimensi...

Are there open-source GitHub repositories related to An Improved K-means Clustering Algorithm Based on EIQ Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems?

Yes, open-source projects like elder-plinius/OBLITERATUS (OBLITERATE THE CHAINS THAT BIND YOU) are actively building upon these concepts.

Which startups are commercializing the technology behind An Improved K-means Clustering Algorithm Based on EIQ Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems?

Products like Mush are bringing this to market. Their focus is: Combine Wi-Fi, Ethernet, and 5G for max download speed.

What other academic literature is closely related to 'An Improved K-means Clustering Algorithm Based on EIQ Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems'?

Yes, highly correlated activity was mapped. An entry titled 'Hybrid K-Nearest Neighbors with Ant Colony Optimization for Securing data warehouses against inferences' discusses this: Data Warehouses (DWs) are among the most powerful technologies for storing and managing large volumes of corporate data, which often include sensit...

How is the concept of 'An Improved K-means Clustering Algorithm Based on EIQ Analysis for Order Batching of Shuttle-Based Storage/Retrieval Systems' being discussed by engineers on Hacker News?

Yes, highly correlated activity was mapped. An entry titled 'Show HN: Turbolite – a SQLite VFS serving sub-250ms cold JOIN queries from S3' discusses this: You might be interested in taking a look at Graft (https://graft.rs/). I have been iterating in this space for the last year, and have learned a lo...

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