logistics_ops · workflow
DoorDash uses ML and mixed-integer optimization to solve last-mile dispatch with DeepRed
DoorDash needed to solve a complex three-sided dispatch problem — matching each order to the right Dasher at the right time while accounting for geography, timing, batching opportunities, supply/demand imbalance, weather, and traffic.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · New order arrives
When a new order arrives, the dispatch engine updates its view of the current marketplace state and the order's relationship to available Dashers.
Tools used
DeepRedGurobiSibyl ML platform
Outcome
DoorDash built DeepRed, an ML-plus-optimization dispatch engine that handles millions of daily orders, continuously improves through simulation and experimentation, and balances efficiency for Dashers with on-time delivery for consumers and merchants.
Results
Volume1% better every day
Source
https://careersatdoordash.com/blog/using-ml-and-optimization-to-solve-doordashs-dispatch-problem/
Grounding & classification
Source type: technical build writeup
20 fields verified against source quotes.
forecastingpredictive analyticsfailure mode describednamed customerproduction runtime claimedsource backedtools describedworkflow describedecommercelogisticscycle time reductionemployee productivitytechnical build writeuplogistics opsorder processingautonomous resolutionextract classify route