Learning-Based Optimisation for Integrated Problems in Intermodal Freight Transport: Preliminaries, Strategies, and State of the Art

Abstract

Intermodal freight transport (IFT) requires a large number of optimisation measures to ensure its attractiveness. This involves numerous control decisions on different time scales, making inte-grated optimisation with traditional methods almost unfeasible. Recently, a new trend in opti-misation science has emerged: the application of Deep Learning (DL) to combinatorial problems. Neural combinatorial optimisation (NCO) enables real-time decision-making under uncertainties by considering rich context information - a crucial factor for seamless synchronisation, optimisa-tion and consequently for the competitiveness of IFT. The objective of this study is twofold. First, we systematically analyse and identify the key actors, operations and optimisation problems in IFT and categorise them into six major classes. Second, we collect and structure the key method-ological components of the NCO framework, including DL models, training algorithms, design strategies, and review the current State of the Art with a focus on NCO and hybrid DL models. Through this synthesis, we integrate the latest research efforts from three closely related fields: optimisation, transport planning and NCO. Finally, we critically discuss, and outline methodo-logical design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable better integration of advanced DL techniques into transport logistics. We hope that this will help re-searchers and practitioners in related fields to expand their intuition and foster the development of intelligent decision-making systems and algorithms for tomorrow's transport systems.