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.
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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
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.
