Optimizing Fleet Efficiency with AI-Driven Dynamic Route Management

Smart routing system to substantially reduce operational costs for a fast-growing business.

DRL Team

AI R&D Center

17 Mar 2025

9 min read

Optimizing Fleet Efficiency with AI-Driven Dynamic Route Management

Content

Summary Tech Challenge Solution Impact

Summary

The client has a fast-growing business with oversized vehicles that use specialized equipment stored in locations spread over a large area. Drivers use this equipment to work at client sites and often need to change their tools during the day.
The client faced challenges in manually planning vehicle routes due to multiple business constraints, such as varying fuel consumption rates, driver workload balancing, equipment availability, and unpredictable events like sudden job cancellations or road conditions. They needed a solution to minimize operational costs and improve effectiveness by dynamically managing routes during the working day.
To address these challenges, we developed a system based on the enhanced Vehicle Routing Problem algorithm and map APIs. This solution reduces transportation costs by optimizing vehicle routes to align with the client’s business objectives, ensuring efficient resource use and real-time adaptability.

Tech Stack

Python

PostgreSQL

Redis

REST API

OR-Tools

OpenStreetMap

Trimble Maps

Google Cloud Platform

Delivery Timeline

2 weeks

Phase 1: System Design and Planning

Solution Architect

2 weeks

Phase 2: Base Routing Algorithm Implementation

Python Developer

Data Engineer

GIS Specialist

2 weeks

Phase 3: Database Setup and Deployment

Database Administrator

DevOps Engineer

2 weeks

Phase 4: Integrating Business Logic Constraints

Python Developer

Data Engineer

GIS Specialist

2 weeks

Phase 5: Algorithm Fine-tuning and Testing

Python Developer x2

2 weeks

Phase 6: Integration with Client Mobile Application

Python Developer

DevOps Engineer

1 week

Phase 7: Deployment and Monitoring

DevOps Engineer

System Administrator

Tech Challenge

Fuel Consumption Optimization: Optimizing routes based on varying vehicle fuel consumption rates. Larger vehicles consume more fuel, especially on certain routes or with heavier loads. Our system had to balance route efficiency and fuel savings by factoring in each vehicle’s specific fuel efficiency, minimizing costs.
Driver Workload Balancing: The system has to assign routes evenly, considering factors like route length, delivery difficulty, and legal driving hours. Dynamic adjustments should help maintain each driver's efficiency.
Equipment Availability: Drivers often needed to switch equipment throughout the day. The system should ensure the right tools are available at the right location, incorporating equipment availability, vehicle capacity, and job site order into the route planning process.
Delivery Timeframes: Clients often require deliveries within specific time windows, adding complexity. The system should prioritize these constraints while optimizing routes for cost and efficiency, ensuring timely deliveries without disrupting other schedules.
Real-Time Route Adjustment: Creating a system that could adapt routes in real-time in response to unpredictable events like unexpected job cancellations, equipment failures, and changing road conditions while maintaining optimal efficiency.
Integration of Real-Time Data: Incorporating live traffic updates and road condition data into the routing algorithm to optimize routes, avoid delays and unsuitable roads, and minimize operational costs like fuel consumption and toll fees.

Solution

Utilizing OR-Tools’ constraint programming, we enabled real-time equipment adjustments across storage locations, improving utilization and reducing driver downtime.
Implemented a logic layer to handle sudden job cancellations or additions, allowing immediate recalculations of routes and driver assignments.
Designed the system to prioritize urgent deliveries, adjusting routes to handle critical tasks promptly.
Leveraged OpenStreetMap and Trimble Maps APIs to incorporate live traffic updates and road conditions, optimizing routes to avoid delays and unsuitable roads.
Built the system to easily adjust to new constraints or changes in business rules, providing long-term adaptability.

Imagine a day in the life of Alex, a driver managing one of the company’s oversized vehicles. Alex begins his morning with a carefully planned route to deliver specialized equipment to various client sites. Midway through the day, a scheduled job is unexpectedly canceled, and a new urgent delivery request comes in. The fleet management system instantly recalculates Alex’s route, removing the canceled stop and seamlessly adding the new task without causing significant delays. As Alex drives, the system continuously monitors real-time traffic data and automatically reroutes him to avoid congestion, ensuring timely deliveries and optimal fuel usage. This dynamic management allows Alex to focus on providing excellent service without the hassle of manual route adjustments.

Impact

Achieved overall route cost minimization by optimizing routes, leading to savings on fuel, driver wages, and toll fees. The client reduced its operating costs by 40 percent.
Eliminated the potential for human errors in route planning by automating the process, ensuring more accurate and efficient operations.
Handling unexpected events and changing conditions allowed the client to maintain high service levels.

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