Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem

Abstract

Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuristics on routing problems has been steadily improving in recent years, but approaches based purely on machine learning are still outperformed by state-of-the-art optimization methods. To close this performance gap, we propose a novel large neighborhood search (LNS) framework for vehicle routing that integrates learned heuristics for generating new solutions. The learning mechanism is based on a deep neural network with an attention mechanism and has been especially designed to be integrated into an LNS search setting. We evaluate our approach on the capacitated vehicle routing problem (CVRP) and the split delivery vehicle routing problem (SDVRP). On CVRP instances with up to 297 customers, our approach significantly outperforms an LNS that uses only handcrafted heuristics and a well-known heuristic from the literature. Furthermore, we show for the CVRP and the SDVRP that our approach surpasses the performance of existing machine learning approaches and comes close to the performance of state-of-the-art optimization approaches.

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• NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
• Efficient Active Search for Combinatorial Optimization Problems
• Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
• Matrix Encoding Networks for Neural Combinatorial Optimization
• Learning for routing: A guided review of recent developments and future directions
• Learning Large Neighborhood Search Policy for Integer Programming
• Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
• Learning to Delegate for Large-scale Vehicle Routing