Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles

TL;DR

This paper introduces a deep reinforcement learning approach for solving the Capacitated Multi-Vehicle Routing Problem, demonstrating near-optimal solutions and outperforming traditional heuristics in large instances.

Contribution

The paper presents a novel DRL-based model with a centralized training and decentralized execution paradigm for CMVRP, capable of generalizing to arbitrary instances without re-training.

Findings

Produces near-optimal solutions for CMVRP

Outperforms common heuristics in large instances

Generalizes to new instances without re-training

Abstract

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.