This talk investigates nodes in a transport network that do not handle transport items but the vehicles circulating to carry passengers or goods between different node work nodes along the network arcs. These nodes are called depots and serve as parking lots or maintenance yards.

Internal depot processes within transport networks face several challenges, like limited vehicle hosting capacity and restricted operation times to name just some predominant examples. The primary need of a depot manager is to coordinate the arriving vehicles with the currently stored vehicles and to ensure that vehicles can leave the depot so early that subsequent vehicle deployments can start as planned. Every depot is a production system comprising several workstations connected by a road or rail infrastructure. Often a depot turns out to be a bottleneck in the transportation processes within a network.

A significant driver for the complexity of planning depot operations arises since vehicles must travel through this production system in a vehicle-specific machine sequence considering vehicle-specific availability and due dates.

We propose a generic mathematical model that aims to connect the vehicle arrivals in front of a depot with vehicle departures from the exit gate by proposing internal vehicle movement processes inside the depot area. The central idea is to set up a time-space network graph where vehicle operations take place. We report initial results from the model application in the context of commuter rail yards confronted with heterogeneous requests to guide vehicles through the depot facilities under time constraints.

In order to manage the demand and control the flow of cargo arriving at terminals, the port sector created a dynamic Truck Appointment System. However, disruptive events can cause a delay or an early arrival of trucks at the port terminal, leading to long waiting times, queues, and the need to reschedule trucks in other time windows when they arrive off the scheduled time. Smart technologies have potential to deal with uncertain scenarios and create a flexible context for the use of TAS. In this context, the main objective of this study is to compare regression and classification Machine Learning algorithms to predict truck arrival times. By comparing the predictions with the original appointment, a flexible Truck Appointment System is designed. Four different ML approaches were evaluated, which have been implemented in Python: Linear Regression, Random Forest, Gradient Boosting Regression, and Decision Tree. Considering the disruptive arrivals, we identified that the classification algorithms performed better than the regression algorithms predicting the ex-act arrival time, but worse than the regression algorithms that predict the time window of truck arrivals.

International rail transport plays an essential role in reducing carbon footprint, with Eurasian rail transport as a successful example. However, multiple interfaces across totally different rail systems increase the complexity of information exchange. The quality of information directly influences the quality of planning. The impact of information exchange and its integration into international rail transport planning activities has not yet been studied extensively. This paper presents an Integrated Planning Method (IPM) for international rail transport focusing on information integration. Advanced mathematical models for Decision Support Systems (DSS) based on information integration are also suggested.

Logistics operations often involve activities of different vehicle classes such as trucks, cars, and scooters. Due to their heterogenous physical properties, the different vehicle classes vary in their impact on the congestion of road networks. In this talk, we study the game-theoretic framework of atomic congestion games with different user classes. In these games, users control traffic composed of different vehicle classes and strive to minimize their travel time in the network. We discuss the existence of pure Nash equilibria in these games. To this end, a set of cost functions is called consistent for this class if all games with cost functions from the set have a pure Nash equilibrium. We give a complete characterization of consistent sets of cost functions showing that the only consistent sets of cost functions are sets of certain affine functions and sets of certain exponential functions. This characterization gives an axiomatic justification of the passager-car-unit concept used frequently in the traffic literature and can be extended to a larger class of games where each atomic player may control flow that belongs to different classes.

A very important task in modern logistics is the estimation of the arrival time of a planned transport. To give precise answers there is a huge demand for accurate traffic prediction models, especially for truck transportation that is performed on public roads. As such, there has been a huge effort to understand congestion both using theoretical models as well as simulations. This work focuses on the theoretical part. For theoretical traffic models, there is strong motivation to push them to be as realistic as possible.

The theoretical traffic model with dynamic time that gained the most attention in recent years is the deterministic fluid queuing model, already introduced by Vickrey.

In this work, we extend the deterministic fluid queuing model with a multi-criteria objective function. We assume that users try to minimize costs subject to arriving at the destination before a given deadline. Here, costs could be thought of as an intrinsic preference a user has regarding the different route choices, and queuing dynamics only play a role in the arrival time of a user.

We determine the existence and the structure of Nash flows over time and fully characterize the price of anarchy for this model, which measures the ratio of the quality of the Nash flow and the optimal flow.

Customer withdrawals from contracts can impact operational efficiency and resource utilization across various logistics scenarios. In the shipping and freight industry, for instance, customers may withdraw from shipping contracts due to shifts in their business needs, changes in market conditions, or unforeseen circumstances. This often necessitates adjustments in cargo consolidation and vehicle assignment. Similarly, in the last-mile delivery for e-commerce, customers may cancel orders or alter delivery preferences, leading to the need for reoptimized delivery routes and adjustments in resource allocation, such as bookings for the vehicle fleet, to ensure efficiency and cost-effectiveness. While individual contract cancellations may create room for new customers (orders, bookings), the paramount concern is the fulfillment of contracts with the remaining customers.

We address the problem of reoptimization with a commitment requirement by introducing the new model of recoverable robust optimization with commitment. More formally, given a combinatorial optimization problem and uncertainty about elements that may fail, we seek a robust solution that, after the failing elements are revealed, can be augmented in a limited way. Importantly, we commit to preserve the remaining elements of the initial solution. We consider different underlying problems and settle the computational complexity of their robust counterparts with commitment. For instance, we show that the reoptimization for the bookings of a vehicle fleet can be solved efficiently.