Navigating uncertainty in Amazon's middle-mile network
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source ↗How Amazon optimizes middle-mile delivery networks under uncertainty - Amazon Science
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Operations research and optimization
Navigating uncertainty in Amazon's middle-mile network
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
By Ruth Misener , Hana Ku , Georgios Paschos
May 6, 2026
8 min read
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Overview by Amazon Nova
Amazon uses advanced optimization techniques and machine learning to design a middle-mile delivery network that can adapt to uncertainty, ensuring reliable delivery promises. The Amazon middle-mile team built tools that (1) simplify network design by identifying consolidation points and (2) use Monte Carlo methods and graph attention networks to enable risk-aware network design. Rather than optimizing for a single forecast, the approach designs networks with built-in optionality, stress-testing them across hundreds of plausible scenarios to ensure resilience under uncertainty. The use of these tools allows Amazon to evaluate network designs across hundreds of plausible scenarios, ensuring that the network remains flexible and resilient under a variety of conditions.
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Before the "last mile" delivery driver sets off for your home, your Amazon item has moved through the middle-mile network of fulfillment centers and sort centers, which brings products close enough to customers to make our same-day or next-day shipping promises possible . For years, Amazon engineers and scientists have been pushing computational boundaries to optimize this network under uncertainty, and that push has accelerated as the network has grown more complex. What happens when a huge snowstorm closes major highways, a sort center is hit by a power outage, or demand for a viral product spikes? These headline disruptions get attention because they're obvious system shocks that vividly illustrate the challenge of planning for uncertainty. But the most important sources of uncertainty are far more subtle: the day-to-day variations in demand and travel times that, if you don't look closely enough, erode efficiency across the entire network.
A snowstorm means two fulfillment centers are operating at reduced capacity, and a sort center is closed, but there’s an unexpected demand for water bottles. How can we best adapt?
We've found that even when we consider just demand variability, optimizing for uncertainty promises potential savings of 0.5%. This is a small percentage, but we obsess over small percentages because real customer experiences lie behind them. And demand variability is just one piece of a puzzle that includes road delays, processing time fluctuations, and countless other microvariations. Months before a customer clicks "Buy Now", Amazon's logistics experts consider a multitude of middle-mile routing questions: What routes should trucks take between warehouses? When should shipments depart? Where should inventory be…
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Notability
notability 4.0/10Amazon blog on logistics; not AI release