Graph neural network-guided optimization for intermodal transport planning under uncertainty

Abstract

This paper addresses the Service Network Design (SND) problem faced by a Logistics Service Provider (LSP), integrating tactical planning with dynamic operational decisions. The proposed approach considers a complex operational setting, including uncertain travel times and demand, dynamic replanning decisions, and interactions between the LSP and carriers. An agent-based simulation model is developed to capture these complexities and is coupled with a metaheuristic to solve the SND. To overcome the high computational cost of simulation, a Graph Neural Network (GNN) is introduced as a surrogate model. The GNN, trained with simulation-generated data, leverages the network structure to predict both global performance (profit) and node-level indicators, which are used to guide the search process. Numerical experiments show that the proposed framework produces high-quality solutions with scalable computational effort, particularly for large-scale instances. The results highlight the potential of integrating optimization and machine learning to address complex freight transport planning.