In this work, we present a Deep Reinforcement Learning-based approach as a solution to one of the popular optimization problems, namely the “Capacitated Vehicle Routing Problem”. We have benchmarked the results against the genetic algorithm and have evaluated the performance using two KPIs- Travelling cost (distance covered) and Computational time. The comparison shows a 5X-20X reduction in cost and a 100X–1000X reduction in computational time. The Deep Reinforcement Learning-based solution adheres to an adaptive learning framework where the system automatically thrives for optimality rather than being explicitly programmed.
