# Learning-Based Optimisation for Integrated Problems in
## 2 Transport Planning Problems in IFT
### Optimisation Problems in Transport Planning  
In this section, our objective is to establish the link between planning problems described in Section 2.1 and combinatorial optimisation problems from the field of OR where NCO techniques are applicable.  
Below, we categorise the most relevant decision problems related to IFT into seven general problem classes. These optimisation problem areas include the following: (i) Network Design Problem [28; 29], (ii) Resource Assignment/Scheduling/Dispatching [25; 30; 31], (iii) Resource Allocation/Rebalancing/Fleet Composition [32; 33; 34], (iv) Vehicle Routing Problem [24; 35; 36], (v) Batching/Sequencing Problem [37; 38], (vi) Prediction/Forecasting [39; 40], and (vii) Online Control Problems with joint decision-making such as inventory management, bidding, and rescheduling [41; 42]. We primarily concentrate on the first six problem classes, excluding Online Control and Joint Decision-Making as it relates more to supply chain and logistics science involving negotiations, bidding, storage, ordering, and shipping policies among multiple stakeholders. However, we include the class Predictions and Forecasting as it is closely interconnected with other strategic and tactical transport planning problems, such as the delay and demand prediction for service network design, which are fundamental subproblems in optimisation frameworks for integrated decision-making.  
Figure 1: Overview of key planning problems and areas in bimodal freight transport (Source: Own diagram).  
The category Network Design Problem involves optimising the number and locations of various types of facilities (nodes), establishing main transport corridors, and defining routes (arcs). Resource Assignment/Scheduling/Dispatching entails the dynamic assignment, selection, scheduling, and dispatching of activities, jobs, or vehicles to resources. This encompasses activities such as scheduling vehicles, assigning trains to routes in the railway network together with precise arrival and departure times, mode selection, or dispatching vehicles to customers or terminals. Resource Allocation/Rebalancing/Fleet Composition addresses the optimal allocation of resources over time, including the distribution and composition of heterogeneous vehicle fleets and the redistribution of empty vehicles or containers across multiple facilities. Vehicle Routing Problem (VRP) aims to find the sequence of nodes/customers (offline) or predict the next node/customer (online) for vehicles under various constraints. As the VRP naturally incorporates fleet management aspects, such as defining the number and type of vehicles at the starting facility, this class may also cover the Vehicle Fleet Problem (VFP), but is only related to a single facility. Batching/Sequencing Problems encompass the operations concerning the storage, picking, sorting, and processing of a group of resources (such as containers, wagons in shunting yards, or orders) within storage or sorting facilities. The items within each batch are then collectively picked along a single pickup route. In fact, these operations have an impact on vehicle routing and on the entire service network. Prediction Problems involve forecasting future events (e.g., outages), timings (e.g., delays or estimated time of arrival), or future demand. This subclass is critical for adequate network and service design planning. Table A1 provides an overview of the most common optimisation problems related to the IFT and the corresponding variants and formulations from the OR perspective.