Cross-industrySolution
Transport Route and Fleet Optimization
Build delivery routes against real constraints (vehicle capacity, driver hours, time windows and traffic), and re-optimize when the day changes rather than absorbing the disruption.
Solution
Machine Learning
Industry
Cross-industry
Company Size
Solution Overview
industry
Cross-industry
solution
Machine Learning
technologies
Azure Machine Learning, Azure Maps, Microsoft Fabric, Power Apps, Power BI, Dynamics 365 Field Service
The Challenge
Delivery routing is built manually or by rules that cannot handle the real constraint set, so vehicles run partly loaded, drivers back-track across the territory and time windows are missed often enough that customers stop trusting them.
- 1Routes built manually or by rules that cannot handle the real constraint set
- 2Vehicles running partly loaded while others are over capacity
- 3Time windows missed often enough that customers stop trusting them
Our Approach
CloudGate models the real constraint set (vehicle capacity and type, driver hours and skills, delivery time windows, access restrictions and live traffic), and generates routes against a configurable objective.
- Constraint model covering capacity, vehicle type, driver hours, time windows and access
- Live traffic and travel time integrated into route generation rather than assumed
- Configurable objective across cost, service level and vehicle utilization
Typical Outcomes
Constraint-complete
CAPACITY, HOURS, WINDOWS AND ACCESS
Re-optimized
WHEN THE DAY CHANGES, NOT PATCHED
Compliant
DRIVER HOURS ENFORCED BY THE MODEL
Typical outcomes for this solution pattern, not the results of a named client engagement.
Technologies Used
Azure Machine LearningAzure MapsMicrosoft FabricPower AppsPower BIDynamics 365 Field Service


