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Machine Learning-Enhanced Cloud-Assisted Last-Mile Delivery Optimization with Priority Management Based on Queueing Theory

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By Yung Chang, Tsai Hsin Jiang
| preprints.org
The rapid growth of e-commerce has escalated the complexity of last-mile delivery, challenging traditional static optimization methods. We propose the Machine Learning-Enhanced Cloud-Assisted Last-Mile Optimization (ML-CALMO) framework, which integrates deep learning, reinforcement learning, and queueing theory to address dynamic demand and priority orders.
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