A GREAT Architecture for Edge-Based Graph Problems Like TSP

• URL: http://arxiv.org/abs/2408.16717v2
• Date: Thu, 26 Jun 2025 13:54:56 GMT
• Title: A GREAT Architecture for Edge-Based Graph Problems Like TSP
• Authors: Attila Lischka, Filip Rydin, Jiaming Wu, Morteza Haghir Chehreghani, Balázs Kulcsár,
• Abstract summary: We propose a novel GNN-based and edge-focused neural model called Graph Edge Attention Network (GREAT)<n>We build a reinforcement learning framework which we apply to Euclidean and non-Euclidean variants of vehicle routing problems.<n>Our framework is among the first to tackle non-Euclidean variants of these problems and competitive results among learning-based solvers.
• Score: 8.317022421446639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the last years, many learning-based approaches have been proposed to tackle combinatorial optimization problems such as routing problems. Many of these approaches are based on graph neural networks (GNNs) or related transformers, operating on the Euclidean coordinates representing the routing problems. However, models operating on Euclidean coordinates are ill-suited for non-Euclidean, asymmetric problem instances that are often found in real-world settings. To overcome this limitation, we propose a novel GNN-based and edge-focused neural model called Graph Edge Attention Network (GREAT). Using GREAT as an encoder to capture the properties of a routing problem instance, we build a reinforcement learning framework which we apply to Euclidean and non-Euclidean variants of vehicle routing problems such as Traveling Salesman Problem, Capacitated Vehicle Routing Problem and Orienteering Problem. Our framework is among the first to tackle non-Euclidean variants of these problems and achieves competitive results among learning-based solvers.

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