Optimizing smart warehousing with genetic algorithm and Deep Q-Learning

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The genetic algorithm reduces order picking times and costs, outperforming random assignment techniques.
The deep Q-learning model enhances operational efficiency by developing dynamic location policies for better performance.
Dynamic dwell points significantly improve order picking time by 44% compared to fixed dwell points.
Optimization of location and dwell policies may lead to substantial gains in logistics performance for warehouses.

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Observational analysis improved order picking efficiency in warehouses, indicating benefits for logistics performance.

Esra Boz (2025) studied this question.

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