CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems

TL;DR

This paper introduces CAMP, a novel multi-agent reinforcement learning approach with attention mechanisms to efficiently solve the complex profiled vehicle routing problem, outperforming existing methods in solution quality and speed.

Contribution

CAMP is the first learning-based method specifically designed for PVRP, incorporating multi-agent collaboration and attention mechanisms for practical vehicle routing applications.

Findings

CAMP achieves competitive results on PVRP variants.

CAMP demonstrates improved computational efficiency.

CAMP outperforms classical neural multi-agent models.

Abstract

The profiled vehicle routing problem (PVRP) is a generalization of the heterogeneous capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes of vehicles to serve client demands subject to different vehicle profiles, with each having a preference or constraint on a per-client basis. While existing learning methods have shown promise for solving the HCVRP in real-time, no learning method exists to solve the more practical and challenging PVRP. In this paper, we propose a Collaborative Attention Model with Profiles (CAMP), a novel approach that learns efficient solvers for PVRP using multi-agent reinforcement learning. CAMP employs a specialized attention-based encoder architecture to embed profiled client embeddings in parallel for each vehicle profile. We design a communication layer between agents for collaborative decision-making across profiled embeddings at each decoding step and a batched pointer mechanism to attend to the profiled embeddings to evaluate the likelihood of the next actions. We evaluate CAMP on two variants of PVRPs: PVRP with preferences, which explicitly influence the reward function, and PVRP with zone constraints with different numbers of agents and clients, demonstrating that our learned solvers achieve competitive results compared to both classical state-of-the-art neural multi-agent models in terms of solution quality and computational efficiency. We make our code openly available at https://github.com/ai4co/camp.