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.

Download Whitepaper

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

Driving Distance

1.5x

Trips to

meet

Tighter SLA

2x

Reduction

Traveling Time

30%

Save

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.

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Key Highlights

Flexible to constraints like split deliveries, specific time delivery and vehicle carrying capacity
Adapt to real time traffic, road condition, weather data and recommend variations in the route near real time
Time and cost efficient as it uses same learned model for multiple similar configurations datasets
Computationally inexpensive as compared to other Deep Learning techniques
Cloud agnostic solution and can be deployed on any cloud
We have observed a 2X-3X reduction in cost and a 100x–1000x reduction in computational time