Navigating uncertainty in Amazon's middle-mile network

Amazon Science · 2 months ago

Amazon developed computational tools combining optimization and machine learning to design its middle-mile logistics network that performs reliably under uncertainty rather than just in perfect conditions. The company created a graph attention network model that represents the network in two interconnected layers to capture spatial demand patterns and interdependencies between origin-destination pairs, enabling it to stress-test designs against hundreds of plausible scenarios. This approach allows Amazon to distinguish between network designs that appear efficient on average but are fragile under disruption and those that maintain stable performance during demand spikes, weather events, or facility outages.