DoorDash uses ML to dynamically optimize Dasher wait times via Auto Order Release
Long Dasher wait times hurt Dasher earnings and merchant experience because restaurants waited for Dashers to arrive before preparing food; the original AOR heuristic used hardcoded store-level geofences with no per-delivery flexibility.
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 · Order confirmed, dispatch kicked off
The merchant confirms the order and the matching and dispatch process is immediately started.
Tools used
LightGBM
Outcome
The ML-based feature dynamically decides when each order should be released to the kitchen, reducing Dasher wait times and improving core delivery metrics.
What failed first
The original Auto Order Release (AOR) heuristic used hardcoded store-level geofences that could not capture actual variance in prep and Dasher arrival times, generated merchant complaints about Dashers waiting, and could not scale without manually defining geofences per store.
Results
Time savedreduction in the wait times that the Dashers were experiencing