Learning Enhanced Optimisation for Routing Problems
This work addresses routing problems, which have many practical applications, by improving solution quality for larger instances that are challenging for existing learning-based algorithms, representing an incremental advancement in the field.
The paper tackles the gap in solution quality between machine learning and operations research algorithms for routing problems by introducing L2GLS, a learning-based approach that uses a penalty term and reinforcement learning to adaptively adjust search efforts, achieving new state-of-the-art results on larger TSP and CVRP instances compared to other machine learning methods.
Deep learning approaches have shown promising results in solving routing problems. However, there is still a substantial gap in solution quality between machine learning and operations research algorithms. Recently, another line of research has been introduced that fuses the strengths of machine learning and operational research algorithms. In particular, search perturbation operators have been used to improve the solution. Nevertheless, using the perturbation may not guarantee a quality solution. This paper presents "Learning to Guide Local Search" (L2GLS), a learning-based approach for routing problems that uses a penalty term and reinforcement learning to adaptively adjust search efforts. L2GLS combines local search (LS) operators' strengths with penalty terms to escape local optimals. Routing problems have many practical applications, often presetting larger instances that are still challenging for many existing algorithms introduced in the learning to optimise field. We show that L2GLS achieves the new state-of-the-art results on larger TSP and CVRP over other machine learning methods.