Applied Timetabling for Railways: Experiences with Several Solution Approaches

Jordi, Julian; Toletti, Ambra; Caimi, Gabrio; Schüpbach, Kaspar

doi:10.29007/w6vc

Cited by 4 publications

(10 citation statements)

References 6 publications

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“…Nonetheless, different methods have been proposed to solve this problem effectively (Borndörfer et al, 2018;Caimi et al, 2017;Liebchen & Möhring, 2007;Sels et al, 2016;Fischetti & Monaci, 2017). In the past, a few challenges have been organized (Artigues et al, 2018;Jordi et al, 2019). In the 2014 ROADEF/EURO challenge for the shunting yard operations, most participants submitted greedy algorithms (Artigues et al, 2018).…”

Section: Railway Timetablingmentioning

confidence: 99%

“…Recently, the SBB challenge (Jordi et al, 2019) motivated the appearance of new approaches ranging from ILP and Answer Set Programming (ASP) (Abels et al, 2020b) to a greedy algorithm. The proposed ILP solution decomposes the problem into two subproblems: routing and scheduling.…”

Section: Railway Timetablingmentioning

confidence: 99%

“…Next, we formalize TSOP based on the description provided by the SBB challenge (Jordi et al, 2019). A railway network R is characterized by a graph (V, E) where V is the set of nodes (representing stopping points in stations, or junctions 5 ) and E is the set of edges (representing sections of railway tracks with the same characteristics).…”

Section: Train Scheduling Optimization Problemsmentioning

confidence: 99%

“…The contribution of this paper is two-fold: (i) a compact MaxSAT encoding for TSOP and TSOP under disruption (TSOPuD); and (ii) a novel iterative MaxSAT algorithm to solve TSOP and TSOPuD. The new encoding is shown to efficiently solve the large data set from the SBB benchmarks (Abels et al, 2020b;Jordi et al, 2019). The proposed iterative algorithm allows the reduction of the size of the problem as we only delay the trains when needed.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Train Scheduling under Disruption with Maximum Satisfiability

Lemos,

Gouveia,

Monteiro

et al. 2024

jair

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This paper proposes an iterative Maximum Satisfiability (MaxSAT) approach designed to solve train scheduling optimization problems. The generation of railway timetables is known to be intractable for a single track. We consider hundreds of trains on interconnected multi-track railway networks with complex connections between trains. Furthermore, the proposed algorithm is incremental to reduce the impact of time discretization. The performance of our approach is evaluated with the real-world Swiss Federal Railway (SBB) Crowd Sourcing Challenge benchmark and Periodic Event Scheduling Problems benchmark (PESPLib). The execution time of the proposed approach is shown to be, on average, twice as fast as the best existing solution for the SBB instances. In addition, we achieve a significant improvement over SAT-based solutions for solving the PESPLib instances. We also analyzed real schedule data from Switzerland and the Netherlands to create a disruption generator based on probability distributions. The novel incremental algorithm allows solving the train scheduling problem under disruptions with better performance than traditional algorithms.

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Section: Railway Timetablingmentioning

confidence: 99%

Section: Railway Timetablingmentioning

confidence: 99%

Section: Train Scheduling Optimization Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iterative Train Scheduling under Disruption with Maximum Satisfiability

Lemos,

Gouveia,

Monteiro

et al. 2024

jair

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“…There are several commercially available railway simulation programs including Trenissimo, LUKS, RailSys, Rail Traffic Controller, and OpenTrack. For a comprehensive review of simulation tools, the reader is referred to research by Medeossi and De Fabris (2), Jordi et al (3), and Nash and Huerlimann (4).…”

Section: Simulation and Railway Planningmentioning

confidence: 99%

Multiobjective Timetable Development Tool for Railway Strategic Planning in Norway

Coviello¹,

Medeossi²,

Nash

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Strategic planning is critical in helping railways develop optimal programs for improving their business by making service more attractive and efficient. Preparing a strategic plan requires comparing multiple alternatives and options, each requiring time-consuming planning, evaluation, and analysis. To improve this process Jernbanedirektoratet, Norway’s Railway Directorate, began a research project to develop a state-of-the-art railway timetable generation tool that could be integrated into the agency’s existing timetable-planning process. The new tool, called Automatic Timetabler with Multiple Objectives, is designed to transform conceptual passenger and freight service requirements into working timetables, while ensuring robustness and minimizing time losses. It is designed to bridge the gap between less detailed (macroscopic) models used in timetable development and detailed (microscopic) traffic simulation models. More specifically, it is a mesoscopic model, simplifying some infrastructure elements while using more detailed representations of others (e.g., using specific track allocations in stations). In practice, the tool quickly provides planners with many feasible and good timetables using high-level timetable requirement data. This ability is very useful for strategic planning because it enables planners to quickly evaluate alternative timetable concepts. This paper describes the new timetable generator tool, its development, and the results of a case study application.

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