Optimization of automated cigarette storage picking based on FP-growth algorithm
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Key Points
Results indicate a significant reduction in warehouse operations, with improvements of 16.8–32.0% in inbound and outbound operations.
The automated warehouse picking optimization method achieved rapid convergence, significantly decreasing picking time by 33.2%.
The study combines an improved genetic algorithm and K-means algorithm to optimize order batching and storage location.
Findings highlight the method's effectiveness in addressing the growing individual demand from e-commerce in warehouse management.
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Implication
This study demonstrates improved operational efficiency through warehouse picking optimization, suggesting enhanced order batching and storage management.