🚚 Logistics & Supply Chain · Data Flow
Fleet Route Optimisation Data Flow
How a delivery company plans daily routes: orders, vehicle capacity, time windows and live traffic feed a route optimiser that assigns stops to drivers.
Drawing diagram…
What this diagram shows
- Orders are geocoded and grouped by delivery window
- The optimiser respects vehicle capacity and driver shifts
- Routes are re-optimised during the day for new orders and traffic
Prompt used
Data flow for route optimisation: overnight orders are geocoded, combined with vehicle capacities, driver shifts, depot locations and customer time windows; the route optimiser (vehicle routing solver) uses a road distance matrix and traffic forecasts to produce routes, which are published to the driver app. During the day, live GPS, traffic and new orders trigger re-optimisation, and actual delivery times feed back to improve travel time estimates.
Mermaid code
flowchart LR ORD[Orders] -->|Addresses| GEO[Geocoding] GEO --> OPT[Route Optimiser - VRP solver] VEH[(Vehicles and capacity)] --> OPT DRV[(Driver shifts)] --> OPT TW[(Customer time windows)] --> OPT MAP[(Road distance matrix, traffic)] --> OPT OPT -->|Routes and stop order| APP[Driver App] APP -->|Live GPS, delivered times| LIVE[Live Tracking] LIVE -->|Delays, new orders| OPT LIVE -->|Actual travel times| MAP
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