Learning to Insert for Constructive Neural Vehicle Routing Solver
This addresses the need for more flexible and higher-quality neural solvers for vehicle routing problems, representing a significant but incremental advance over existing constructive methods.
The paper tackles the suboptimal results of existing constructive neural combinatorial optimization methods for vehicle routing problems by proposing L2C-Insert, a novel learning-based method that builds solutions by inserting unvisited nodes at any valid position in partial solutions, achieving superior performance on TSP and CVRP instances across various problem sizes.
Neural Combinatorial Optimisation (NCO) is a promising learning-based approach for solving Vehicle Routing Problems (VRPs) without extensive manual design. While existing constructive NCO methods typically follow an appending-based paradigm that sequentially adds unvisited nodes to partial solutions, this rigid approach often leads to suboptimal results. To overcome this limitation, we explore the idea of insertion-based paradigm and propose Learning to Construct with Insertion-based Paradigm (L2C-Insert), a novel learning-based method for constructive NCO. Unlike traditional approaches, L2C-Insert builds solutions by strategically inserting unvisited nodes at any valid position in the current partial solution, which can significantly enhance the flexibility and solution quality. The proposed framework introduces three key components: a novel model architecture for precise insertion position prediction, an efficient training scheme for model optimization, and an advanced inference technique that fully exploits the insertion paradigm's flexibility. Extensive experiments on both synthetic and real-world instances of the Travelling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) demonstrate that L2C-Insert consistently achieves superior performance across various problem sizes.