Artificial Intelligence Practices

Solution Approach to Solve Capacitated Vehicle Routing Problem, Using Deep Reinforcement Learning

Increase throughput and reduce turnaround time by up to 2x.
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Leverage our novel solution to resolve the Capacitated Vehicle Routing Problem (CVRP) using minimal data and exploration.

Capacitated Vehicle Routing Problem (CVRP) is one of the most important models in the logistics field. It has many applications in various industries, including healthcare, manufacturing, eCommerce, logistic, transportation, etc. Particularly, it has been widely applied to transportation logistics problems. However, solving CVRP is still challenging and requires a lot of data and exploration. In this whitepaper, we have discussed a state-of-the-art AI-driven solution that effectively solves CVRP with demonstrated key aspects in each level of the solution approach.

Impact Numbers

50%

Reduction

in

Driving Distance

1.5x

Trips to

meet

Tighter SLA

2x

Reduction

in

Traveling Time

30%

Save

in

Transportation

Costs

While most commercial solutions to real-world CVRPs tend to adopt a meta-heuristic approach, we propose a novel hybrid strategy that leverages Deep Reinforcement Learning and demands considerably less time and resources.

Great! Let's continue the conversation with our AI experts to know how they can help solve your business problem.

Key Highlights

Key Highlights

Potential Use Cases

Helps the logistic companies to

plan delivery and meet tighter SLA

to improve customer experience.

Efficient for fleet management

organizations to finalize the route

planner.

Works effectively for an

eCommerce company in route planning aspects.

Handy for applications that

facilitate online vehicle sharing

options for users.

Affine's approach to the Capacitated Vehicle Routing Problem leverages a novel and intuitive solution to demonstrate its success in a range of scenarios!

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Manas Agrawal

CEO & Co-Founder

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