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

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.