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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
Full architecture and data flow in the Data & AI Catalog

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