Demand for railway transportation keeps on growing. Therefore, a thorough understanding of the capacity of railway networks is crucial. In this paper, the well-known compression method based on max-plus algebra is extended. A number of challenges are addressed to apply this compression method to large and complex networks, such as the one considered in this paper. Some trains have to be split artificially, while keeping the parts together during the compression. The trains should also be ordered explicitly, since there is no part of the infrastructure used by all trains. The results in this paper indicate that it is possible to thoroughly analyse the capacity by the adjusted compression method for large and complex networks, but the results should be interpreted with care. The results show, for instance, that the capacity occupation heavily depends on the size of the network that is considered and that it is not easy to give a clear, practical interpretation of the capacity occupation. Nevertheless, the method allowed to determine a number of critical paths and, even more importantly, a number of critical resources in the zones considered.@en

Traffic Management and Logistic Optimization have been extensively studied as two separate classes of problems, for which numerous methodologies, mathematical models and algorithmic solutions were made available in literature. However, little attention has been devoted to the interactions between the variables involved in these problems and the consequences of the decision making processes carried independently by Traffic Managers and Logistic Players. We believe this to be of considerable importance, since partial or incomplete knowledge on one another's decisions might yield sub-optimality for either or both of them. In this work, we propose an integrated view on both classes of problems, providing mathematical formulations to support the assessment of the impact which the two players may have on each other.@en

Traffic Management and Logistic Optimization have been extensively studied as two separate classes of problems, for which numerous methodologies, mathematical models and algorithmic solutions were made available in literature. However, little attention has been devoted to the interactions between the variables involved in these problems and the consequences of the decision making processes carried independently by Traffic Managers and Logistic Players. We believe this to be of considerable importance, since partial or incomplete knowledge on one another's decisions might yield sub-optimality for either or both of them. In this work, we propose an integrated view on both classes of problems, providing mathematical formulations to support the assessment of the impact which the two players may have on each other.@en

Traffic Management and Logistic Optimization have been extensively studied as two separate classes of problems, for which numerous methodologies, mathematical models and algorithmic solutions were made available in literature. However, little attention has been devoted to the interactions between the variables involved in these problems and the consequences of the decision making processes carried independently by Traffic Managers and Logistic Players. We believe this to be of considerable importance, since partial or incomplete knowledge on one another's decisions might yield sub-optimality for either or both of them. In this work, we propose an integrated view on both classes of problems, providing mathematical formulations to support the assessment of the impact which the two players may have on each other.@en

In practice, unexpected events frequently cause delays, often leading to conflicts, since multiple trains simultaneously require the same infrastructure. Currently, such conflicts are manually resolved by dispatchers, although it is impossible for them to anticipate the impact of their actions on the entire network. Conflict detection and resolution tools can help dispatchers make informed decisions. This research introduces a new heuristic that uses rescheduling and rerouting in station areas and is innovative due to strong similarities with real-life situations. The algorithm for rescheduling is based on limiting the total delay caused by conflicts, through analyzing the predicted progress over the following hour. If the conflict arises in a station area, an optimization procedure checks first whether rerouting leads to a solution with (almost) no delays. Because some trains have a needless amount of alternative routes, a routing filter is applied before the rerouting procedure. This rerouting procedure is based on a flexible job shop problem aiming to minimize the actual travel time of passengers. This fast and effective method is experimentally tested using a close-to-practice simulation tool and compared to commonly used dispatching rules. @en

In practice, unexpected events frequently cause delays, often leading to conflicts, since multiple trains simultaneously require the same infrastructure. Currently, such conflicts are manually resolved by dispatchers, although it is impossible for them to anticipate the impact of their actions on the entire network. Conflict detection and resolution tools can help dispatchers make informed decisions. This research introduces a new heuristic that uses rescheduling and rerouting in station areas and is innovative due to strong similarities with real-life situations. The algorithm for rescheduling is based on limiting the total delay caused by conflicts, through analyzing the predicted progress over the following hour. If the conflict arises in a station area, an optimization procedure checks first whether rerouting leads to a solution with (almost) no delays. Because some trains have a needless amount of alternative routes, a routing filter is applied before the rerouting procedure. This rerouting procedure is based on a flexible job shop problem aiming to minimize the actual travel time of passengers. This fast and effective method is experimentally tested using a close-to-practice simulation tool and compared to commonly used dispatching rules. @en

In practice, unexpected events frequently cause delays, often leading to conflicts, since multiple trains simultaneously require the same infrastructure. Currently, such conflicts are manually resolved by dispatchers, although it is impossible for them to anticipate the impact of their actions on the entire network. Conflict detection and resolution tools can help dispatchers make informed decisions. This research introduces a new heuristic that uses rescheduling and rerouting in station areas and is innovative due to strong similarities with real-life situations. The algorithm for rescheduling is based on limiting the total delay caused by conflicts, through analyzing the predicted progress over the following hour. If the conflict arises in a station area, an optimization procedure checks first whether rerouting leads to a solution with (almost) no delays. Because some trains have a needless amount of alternative routes, a routing filter is applied before the rerouting procedure. This rerouting procedure is based on a flexible job shop problem aiming to minimize the actual travel time of passengers. This fast and effective method is experimentally tested using a close-to-practice simulation tool and compared to commonly used dispatching rules. @en

Though timetabling can account for some possible delays, in practice, external events still regularly lead to delays. Once trains are deviating from their schedule, conflicts can occur. A conflict implies that (at least) two trains require the same part of the infrastructure at the same time. Conflicts need to be resolved quickly in a way that disturbs the system as little as possible. Therefore, the impact on the whole network should be taken into account when solving conflicts. This paper discusses a heuristic conflict prevention technique capable of solving multiple conflicts together by reordering, retiming and locally rerouting trains. Based on a close-to-practice simulation tool where a simplified prediction horizon up to 10 minutes is considered, this technique is compared to FCFS. Results show significant improvements when comparing to FCFS.@en