Route Optimization

The Route Optimization engine uses AI to calculate the most efficient delivery sequences for single or multiple vehicles, minimizing travel time, fuel consumption, and operational cost while satisfying all delivery constraints.

What the Engine Optimizes

Objective	Description
Travel time	Minimize total duration including driving and service time at each stop
Distance	Reduce total kilometers driven across all vehicles
Fuel cost	Weight routes by road type and vehicle fuel efficiency profile
On-time delivery	Maximize deliveries completed within their required time windows
Driver hours	Stay within Hours of Service (HOS) limits per driver

Key Constraints

The optimizer respects all operational constraints defined in your resources:

• Time windows — Each stop can have an earliest arrival and a latest arrival time
• Vehicle capacity — Weight (kg) and volume (m³) limits per vehicle
• Vehicle type restrictions — Some stops may require specific vehicle types (e.g., refrigerated, flatbed)
• Driver skills — Stops can require certified drivers (e.g., hazmat, ADR)
• Maximum route duration — Configurable per fleet or per order configuration
• Mandatory breaks — HOS-compliant rest periods inserted automatically

How to Use Route Optimization

From the Orders View

1. Select one or more unassigned orders in Operations → Orders
2. Click AI Optimize Routes
3. Configure the optimization parameters:
   • Optimization objective (time / distance / cost)
   • Available vehicles and drivers
   • Planning horizon (today, next 24h, custom date range)
4. Review the proposed route plan with estimated times and projected savings
5. Click Apply — the system creates assignments and dispatches via the Orchestrator

From the Orchestrator

Route Optimization is available as an engine selection in the Orchestrator:

1. Open Operations → Orchestrator
2. Select the orders to include
3. Choose AI Route Optimizer as the dispatch engine
4. Run the optimization and review manifests before committing

Optimization Output

After running, the engine returns:

• Ordered stop sequence per vehicle with turn-by-turn directions
• Estimated arrival time at each stop
• Estimated fuel consumption per route
• Savings summary — time saved vs. manual sequencing, km saved, estimated CO₂ reduction
• Unserviceable stops — stops that could not be scheduled within constraints, with the specific reason

Dynamic Rerouting

When a vehicle is already en route, the AI monitors live conditions and triggers a dynamic reoptimization when needed:

Trigger	Action
Traffic incident detected	Recalculate remaining stops with alternate roads
Stop cancelled mid-route	Resequence remaining stops optimally
New urgent order added	Insert stop at the optimal position in the active route
Vehicle breakdown reported	Redistribute remaining stops to other available vehicles

Dynamic rerouting notifications are sent automatically to the driver via the FM Mobile app.

Performance and Scale

Fleet Size	Optimization Time	Typical Distance Reduction
Up to 10 vehicles, 100 stops	< 5 seconds	15–25%
10–50 vehicles, 500 stops	10–30 seconds	12–20%
50+ vehicles, 1000+ stops	30–90 seconds	10–18%

Improvement percentages are compared against sequential (first-come-first-served) dispatch. Results vary based on geographic density and constraint complexity.

Integration with Telematics

When a telematics provider is connected, the optimizer uses enriched real-world data:

• Real-time traffic from the connected telematics provider
• Historical traffic patterns by day of week and time of day
• Vehicle speed profiles from actual recorded driving history
• Fuel consumption curves per vehicle model derived from telemetry

This produces route time estimates significantly more accurate than generic mapping APIs.