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Article

An Evaluation Framework and Algorithms for Train Rescheduling

Department of Computer Science, Blekinge Institute of Technology, 37141 Karlskrona, Sweden
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Algorithms 2020, 13(12), 332; https://doi.org/10.3390/a13120332
Received: 26 October 2020 / Revised: 4 December 2020 / Accepted: 7 December 2020 / Published: 11 December 2020
(This article belongs to the Special Issue Algorithms in Decision Support Systems)
In railway traffic systems, whenever disturbances occur, it is important to effectively reschedule trains while optimizing the goals of various stakeholders. Algorithms can provide significant benefits to support the traffic controllers in train rescheduling, if well integrated into the overall traffic management process. In the railway research literature, many algorithms are proposed to tackle different versions of the train rescheduling problem. However, limited research has been performed to assess the capabilities and performance of alternative approaches, with the purpose of identifying their main strengths and weaknesses. Evaluation of train rescheduling algorithms enables practitioners and decision support systems to select a suitable algorithm based on the properties of the type of disturbance scenario in focus. It also guides researchers and algorithm designers in improving the algorithms. In this paper, we (1) propose an evaluation framework for train rescheduling algorithms, (2) present two train rescheduling algorithms: a heuristic and a MILP-based exact algorithm, and (3) conduct an experiment to compare the two multi-objective algorithms using the proposed framework (a proof-of-concept). It is found that the heuristic algorithm is suitable for solving simpler disturbance scenarios since it is quick in producing decent solutions. For complex disturbances wherein multiple trains experience a primary delay due to an infrastructure failure, the exact algorithm is found to be more appropriate. View Full-Text
Keywords: algorithm evaluation; decision support systems; parallel algorithms; multi-objective optimization; train rescheduling algorithm evaluation; decision support systems; parallel algorithms; multi-objective optimization; train rescheduling
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MDPI and ACS Style

Josyula, S.P.; Krasemann, J.T.; Lundberg, L. An Evaluation Framework and Algorithms for Train Rescheduling. Algorithms 2020, 13, 332. https://doi.org/10.3390/a13120332

AMA Style

Josyula SP, Krasemann JT, Lundberg L. An Evaluation Framework and Algorithms for Train Rescheduling. Algorithms. 2020; 13(12):332. https://doi.org/10.3390/a13120332

Chicago/Turabian Style

Josyula, Sai P., Johanna T. Krasemann, and Lars Lundberg. 2020. "An Evaluation Framework and Algorithms for Train Rescheduling" Algorithms 13, no. 12: 332. https://doi.org/10.3390/a13120332

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