# Learning-Based Optimisation for Integrated Problems in
## 1 Introduction  
The challenges faced by intermodal freight transport (IFT) encompass synchronisation processes between multiple decision-makers, external barriers, and the lack of integration between different modes [1]. The attractiveness and effectiveness of IFT are therefore directly affected by the degree of synchronisation and optimisation across the entire logistics chain. Improving the performance of intermodal transport requires fast optimisation techniques capable of finding solutions for multiple problems simultaneously at various planning levels, taking into account a highly dynamic and uncertain environment. In general, transport planning problems have been a key area of interest in optimisation science for decades, resulting in a wide range of solution methods. The conventional optimisation techniques from the field of operational research (OR) are known to be computationally expensive, especially when applied in dynamic environments [2]. Furthermore,classical OR mainly focuses on the formulation and solution of isolated combinatorial problems, often abstracted from the complexities of real-world, integrated transport planning applications. For instance, the Job Shop Scheduling Problem (JSSP), a classical combinatorial problem in optimisation science, deals with the efficient allocation of resources to activities under a set of constraints. From the transport planning perspective, JSSP solution methods can be applied in terminals to assign cranes or vehicles to containers, but also in train routing, where a resource denotes a subset of infrastructure elements that are exclusively assigned to a single train at a given time [3]. Obviously, despite different transport planning contexts, the solution methods are often problem agnostic and transferable to different application scenarios. Furthermore, solution methods for operational problems, such as itinerary replanning, demand a combination of different techniques [4]. Today, these problems are mostly handled within the framework of the linear programming paradigm, often encapsulated, and focused on one-dimensional optimisation problems such as the JSSP or the Travelling Salesman Problem (TSP). To improve the overall performance of intermodal transport systems, future optimisation frameworks must be highly integrated, computationally efficient, consider rich context, integrate multiple objectives, and handle uncertainties. Currently, there is a growing effort among scholars to apply learning-based methods into, alongside, or as substitutes for the canonical linear programming paradigm, as these approaches are better suited to the needs posed by modern optimisation tasks [5; 6; 7; 8]. In this context, this study aims to address three research questions:  
* IFT is a complex, integrated system involving multiple decision-makers with different objectives that need to be optimised in parallel. What are the key operational, tactical, and strategic planning problems in IFT and how can they be mapped to mathematical problem formulations commonly used from an OR perspective? How can these problems be categorised in a structured and systematic manner? * Learning-based algorithms for combinatorial optimisation problems represent a very promising area for seamless and fast decision-making under uncertainties--a crucial factor for enhancing the overall IFT performance. Over the past five years, research in the area of computer science has converged on methods referred to as Neural Combinatorial Optimisation (NCO), which apply Transformer architecture [10] to combinatorial problems. However, a unified and structured overview of key methods, strategies, and framework setups is lacking in the current State of the Art. This raises the question: what are the key methodological components, algorithm setups and solution strategies in NCO and how can they be structured and abstracted to assist future research in building decision-making tools for integrated transport planning problems?