Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles
This addresses routing efficiency for logistics and transportation industries, but it is incremental as it builds on existing multi-agent DRL approaches.
The paper tackles the Capacitated Multi-Vehicle Routing Problem with a fixed fleet size by proposing a deep reinforcement learning model that uses centralized training and decentralized execution, achieving near-optimal solutions and outperforming common heuristics in large instances without needing retraining for arbitrary cases.
Motivated by the promising advances of deep-reinforcement learning (DRL) applied to cooperative multi-agent systems we propose a model and learning procedure to solve the Capacitated Multi-Vehicle Routing Problem (CMVRP) with fixed fleet size. Our learning procedure follows a centralized-training and decentralized-execution paradigm. We empirically test our model and showed its capability for producing near-optimal solutions through cooperative actions. In large instances, our model generates better solutions than other commonly used heuristics. Additionally, our model can solve arbitrary instances of the CMVRP without requiring re-training.