Learning-Enhanced Neighborhood Selection for the Vehicle Routing Problem with Time Windows

Abstract

Large Neighborhood Search (LNS) is a universal approach that is broadly applicable and has proven to be highly efficient in practice for solving optimization problems. We propose to integrate machine learning (ML) into LNS to assist in deciding which parts of the solution should be destroyed and repaired in each iteration of LNS. We refer to our new approach as Learning-Enhanced Neighborhood Selection (LENS for short). Our approach is universally applicable, i.e., it can be applied to any LNS algorithm to amplify the workings of the destroy algorithm. In this paper, we demonstrate the potential of LENS on the fundamental Vehicle Routing Problem with Time Windows (VRPTW). We implemented an LNS algorithm for VRPTW and collected data on generated novel training instances derived from well-known, extensively utilized benchmark datasets. We trained our LENS approach with this data and compared the experimental results of our approach with two benchmark algorithms: a random neighborhood selection method to show that LENS learns to make informed choices and an oracle neighborhood selection method to demonstrate the potential of our LENS approach. With LENS, we obtain results that significantly improve the quality of the solutions.