Abstract

The exponential growth of e-commerce has intensified reverse logistics challenges, with product returns generating substantial carbon emissions through inefficient routing and processing. This research proposes an integrated architecture combining edge-deployed convolutional neural networks (CNNs) for real-time return quality assessment with Deep Reinforcement Learning (DRL) for carbon-aware dynamic routing optimization. The edge vision module classifies returned items into disposition categories with sub-second latency; these assessments feed directly into a DRL optimizer formulated as a Markov Decision Process (MDP). Simulation experiments on benchmark VRPSDP instances demonstrate 18–23% carbon emission reduction, 94.2% classification accuracy, and 67% lower decision latency compared to cloud-based alternatives. A six-month pilot with two retail partners validating 78,000 returns confirms operational viability with 99.2% system uptime. This is the first end-to-end framework integrating edge AI vision with DRL-based carbon-optimized routing for retail reverse logistics.

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