@article{hanou_multi-agent_2025,

title = {Multi-Agent Pathfinding for Railway Routing: RailDresden 2025: 11th International Conference on Railway Operations Modelling and Analysis},

author = {I. Hanou and M. de Weerdt},

url = {https://tu-dresden.de/bu/verkehr/die-fakultaet/veranstaltungen/raildresden2025/ressourcen/dateien/BoA_RailDresden_full_final.pdf?lang=en},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

pages = {102–102},

abstract = {Research in railway operations has mostly focused on operations research methods. However, these real-world problems have a state-based nature, which makes them very suitable for AI models, such as the Multi-Agent Pathfinding problem, where agents move in a grid and need to be routed from their start to their goal location without colliding with each other. The core aspect of problems like train shunting and train dispatching is routing, which is often not the main focus of current mathematical formulations. Therefore, we apply the state-of-the-art algorithms to the railway problems of shunting and dispatching and study their usability for routing trains. The Multi-Agent Pathfinding problem is often solved with one of two algorithms: conflict-based search (a two-stage algorithm detecting conflicts between individual paths and using A* search to find new conflict-free paths), and branch-cut-and-price (a linear program adding cuts (row generation) based on problem-specific constraints, and finding new paths to be selected that satisfy all constraints using a pricer). We modify these algorithms to include more railway details. First, we allow for the matching of train units (i.e., ensure the necessary train units of a certain type are available for departure) by specifying goals for agent (type) groups instead of single agent goals. Moreover, we add goal sequences for servicing stations and agents of different sizes, and we study specific aspects of the railway infrastructure to exploit in the algorithm. Finally, we show the use of Multi-Agent Pathfinding solvers in different railway settings and analyze the conditions for success.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kemmeren_introducing_2025,

title = {Introducing flexibility in any-start-time safe interval path planning},

author = {E. Kemmeren},

url = {https://repository.tudelft.nl/record/uuid:04890fe5-d983-4b91-90bd-dc5da7129161},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

abstract = {During the daily operation of the railway network, ProRail is responsible for handling delays and planning ad hoc train movements. Train handling documents aid the traffic controllers in common situations. But when multiple trains are delayed, and these documents do not apply, they are left to their own expertise. <br/>In this thesis, we introduce FlexSIPP, an algorithm to plan or replan agents in an existing multi-agent plan. FlexSIPP builds upon the prior works of any-start-time safe interval path planning, where the current routes of the agents are seen as moving obstacles. FlexSIPP loosens this restriction by introducing flexibility: the ability for an agent to delay its plan while minimally impacting other agents. <br/>This algorithm is evaluated on the Dutch railway network. By finding tipping points, that is, the moment it is better to switch the order of two trains on the track to minimize the delay, we can recreate train handling documents. We show that FlexSIPP finds the same solutions within a minute in the case that no other trains are delayed. This implies that FlexSIPP is also able to aid traffic controllers in the case that other trains are delayed},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hanou_replanning_2024,

title = {Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub},

author = {I. K. Hanou and D. W. Thomas and W. Ruml and M. de Weerdt},

url = {https://ojs.aaai.org/index.php/ICAPS/article/view/31483},

doi = {10.1609/icaps.v34i1.31483},

issn = {2334-0843},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

journal = {Proceedings of the International Conference on Automated Planning and Scheduling},

volume = {34},

pages = {258–266},

abstract = {Train routing is sensitive to delays that occur in the network. When a train is delayed, it is imperative that a new plan be found quickly, or else other trains may need to be stopped to ensure safety, potentially causing cascading delays. In this paper, we consider this class of multi-agent planning problems, which we call Multi-Agent Execution Delay Replanning. We show that these can be solved by reducing the problem to an any-start-time safe interval planning problem. When an agent has an any-start-time plan, it can react to a delay by simply looking up the precomputed plan for the delayed start time. We identify crucial real-world problem characteristics like the agent's speed, size, and safety envelope, and extend the any-start-time planning to account for them. Experimental results on real-world train networks show that any-start-time plans are compact and can be computed in reasonable time while enabling agents to instantly recover a safe plan.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lonyuk_using_2024,

title = {Using PDDL models to solve TUSS},

author = {N. Lonyuk},

url = {https://repository.tudelft.nl/record/uuid:242e8fdd-95d2-4915-bd28-f0697559514e},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

abstract = {Due to the increased demand for train travel, train operators are considering increasing their rolling stock. Before achieving this, they must enhance the capacity of their shunting yards. This is attempted by improving methodologies for solving the Train Unit Shunting and Servicing (TUSS) problem. To address the TUSS problem, a planner determines routes on shunting yards for trains, ensuring they visit designated service tracks before parking in a configuration that facilitates a smooth departure. <br/>TUSS is a well-studied problem, and various approaches have been proposed. The first approach capable of solving real-world, complete TUSS instances is a local search method introduced by van den Broek et al. In this thesis, we explore an alternative approach using PDDL models. PDDL is the standard language for describing Automated Planning problems. Automated Planning is a well-established field within artificial intelligence, and new, improved algorithms are continually developed to solve PDDL models for problems similar to TUSS.<br/>In this thesis, we design a detailed model in PDDL and propose several methods to simplify the model so that planning algorithms perform more efficiently compared to the detailed model. When solving simplified models, a post-processing routine is employed to generate detailed shunting plans. The performance of several model-independent PDDL planners was analysed, and the best-performing planner was identified as Temporal FastDownward. <br/>By analysing plans obtained from experiments, we identified areas for improvement. Based on this knowledge, we developed a new TUSS-specific planner called Train Order Preserving Search (TOPS). TOPS employs a search algorithm with effective pruning of symmetrical states and a custom heuristic that guides the search towards states where the order of trains aligns with the departure order. TOPS significantly outperformed Temporal FastDownward in these experiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}