CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems

2025-01-06

AI Summary

This work addresses the Profile-aware Vehicle Routing Problem (PVRP), a heterogeneous VRP variant incorporating vehicle profiles, customer preferences, and regional constraints. We propose the first end-to-end learnable PVRP solver based on Multi-Agent Reinforcement Learning (MARL), specifically designed for this complex, constrained optimization setting. Our method features: (i) a profile-aware parallel attention encoder that captures vehicle-specific characteristics; (ii) a communication layer enabling cross-profile coordination among agents; and (iii) a batched pointer decoding mechanism for efficient solution construction. By integrating profile-sensitive embeddings with attention-driven multi-agent collaboration, our approach achieves state-of-the-art performance on two PVRP benchmarks—matching or surpassing existing neural solvers in both solution quality and inference efficiency. The implementation is publicly available.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Distributed Problem Solving
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Vehicle Routing Problem
• Collaborative Attention Model
• Role-based Card Optimization

Innovation

• CAMP
• PVRP
• Role-aware Collaborative Attention