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

Elija Deineko · Carina Kehrt

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transportation and logistics systems Maritime Ports and Logistics Vehicle Routing Optimization Methods

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 integrated optimisation with traditional methods almost unfeasible. Recently, a new trend in optimisation 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, optimisation, 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 methodological 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 methodological design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable a better integration of advanced DL techniques into transport logistics. We hope that this will help researchers 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.

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2013Multimodal freight transportation planning: A literature review 2008Transit network design and scheduling: A global review 2022A review on reinforcement learning algorithms and applications in supply chain management 2011Facility location dynamics: An overview of classifications and applications 2018A unified framework for stochastic optimization 2021A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization 2021Deep reinforcement learning for inventory control: A roadmap 2019A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

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