Learning large neighborhood search for maritime inventory routing optimization

Abstract Maritime inventory routing optimization is an important yet challenging combinatorial optimization problem. We propose a machine learning–based local search approach for finding feasible solutions to large‐scale maritime inventory routing optimization problems. Given the combinatorial complexity of the problems, we integrate a graph neural network–based neighborhood selection method to enhance local search efficiency. Our approach enables a structured exploration of different neighborhoods by imitating an optimization‐based expert neighborhood selection policy, improving solution quality while maintaining computational efficiency. Through extensive computational experiments on realistic instances, we demonstrate that our method outperforms direct mixed‐integer programming as well as benchmark local search approaches in solution time and solution quality.