Guided Local Search with an Adaptive Neighbourhood Size Heuristic for Large Scale Vehicle Routing Problems

The Large-Scale Vehicle Routing Problem is an NP-hard combinatorial optimisation problem with many challenges regarding the increasing number of possible solutions. To reduce the search space, limiting the neighbourhood size for the neighbourhood search approaches is a commonly used strategy to reach a good balance between efficiency and effectiveness. However, it lacks generalisability, since setting a fixed neighbourhood limit might be below optimal for certain instances. In this work, a heuristic method that automatically changes the neighbourhood size is proposed. The heuristic increases or decreases the search scope of the neighbourhood search operators to better match the search process. It does that by looking at the moving trajectories of the previous iteration. We combine the proposed online neighbourhood size adaption heuristic with the highly-efficient Knowledge-Guided Local Search (KGLS) and successfully achieved up to almost 40% improvement to the efficiency. Furthermore, the experiment results show that the KGLS with the adaptive neighbour size heuristic can obtain statistically better solutions on 50 out of the 110 instances compared, while worse on only 25 instances.