Navigating uncertainty in Amazon's middle-mile network
Amazon Science
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.
Why it matters
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
