Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms

• URL: http://arxiv.org/abs/2504.06126v2
• Date: Sat, 20 Sep 2025 17:28:21 GMT
• Title: Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms
• Authors: Ido Greenberg, Piotr Sielski, Hugo Linsenmaier, Rajesh Gandham, Shie Mannor, Alex Fender, Gal Chechik, Eli Meirom,
• Abstract summary: Vehicle Routing Problems (VRP) are a fundamental NP-hard challenge in Evolutionary optimization.<n>We introduce an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions.<n>This framework consistently outperforms current state-of-the-art solvers across various time budgets.
• Score: 53.75036695728983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vehicle Routing Problems (VRP) are an extension of the Traveling Salesperson Problem and are a fundamental NP-hard challenge in combinatorial optimization. Solving VRP in real-time at large scale has become critical in numerous applications, from growing markets like last-mile delivery to emerging use-cases like interactive logistics planning. In many applications, one has to repeatedly solve VRP instances drawn from the same distribution, yet current state-of-the-art solvers treat each instance on its own without leveraging previous examples. We introduce an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm. This framework, Evolutionary Algorithm with Reinforcement Learning Initialization (EARLI), consistently outperforms current state-of-the-art solvers across various time budgets. For example, EARLI handles vehicle routing with 500 locations within one second, 10x faster than current solvers for the same solution quality, enabling real-time and interactive routing at scale. EARLI can generalize to new data, as we demonstrate on real e-commerce delivery data of a previously unseen city. By combining reinforcement learning and genetic algorithms, our hybrid framework takes a step forward to closer interdisciplinary collaboration between AI and optimization communities towards real-time optimization in diverse domains.

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