Learn to Design the Heuristics for Vehicle Routing Problem
This work addresses the challenge of designing efficient heuristics for vehicle routing, eliminating the need for expert knowledge and handcrafted designs, though it is incremental in improving upon existing neural optimization approaches.
The paper tackles the Vehicle Routing Problem by learning local-search heuristics through a neural network, achieving better performance than traditional and existing neural methods on medium-scale datasets and handling large-scale datasets with over 400 nodes.
This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a destroy operator that destructs a candidate solution, and a following repair operator that rebuilds the destructed one into a new one. The proposed neural network, as trained through actor-critic framework, consists of an encoder in form of a modified version of Graph Attention Network where node embeddings and edge embeddings are integrated, and a GRU-based decoder rendering a pair of destroy and repair operators. Experiment results show that it outperforms both the traditional heuristics algorithms and the existing neural combinatorial optimization for VRP on medium-scale data set, and is able to tackle the large-scale data set (e.g., over 400 nodes) which is a considerable challenge in this area. Moreover, the need for expertise and handcrafted heuristics design is eliminated due to the fact that the proposed network learns to design the heuristics with a better performance. Our implementation is available online.