Deep Reinforcement Learning

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Deep Reinforcement Learning may bring more efficient Deliveries

Truck doing a delivery

In the beginning it was sufficient to get the parcel to the destination. Highwaymen, think Dick Turpin, would regularly empty carriages as they travelled between destinations. Next came the next day service, ensuring parcels were collected and delivered successfully. Adding in different services brought mixture and variety. Then came the realisation that costs needed to be reduced to stay competitive, save fuel, and also protect the environment.

The Travelling Salesman Problem and Vehicle Routing Problems, are long standards in problems that need solutions. Each new solution has brought it’s rewards. The team of Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč have released their paper on their solution to the Vehicle Routing Problem using Deep Reinforcement Learning.

The solution is not better than the recent solution to TSP, but comes at a faster time for near identical solution. Their approach outperforming classical solutions in quality and computational time. All that it requires is some training. With an added advantage that it is scalable and doesn’t require a distance matrix calculation, and copes with vehicles of different sizes, and multiple loading back at the depot, and differing demands by the delivery points.

Next time you’re waiting in for that delivery, it may just be that the vehicle has been re-routed.

#telanovaReporter

Download the full paper from Cornell University

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