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A Fuzzy Approach for Evaluation of Reconfiguration Actions After Unwanted Events in the Railway System

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Engineering in Dependability of Computer Systems and Networks (DepCoS-RELCOMEX 2019)

Abstract

Due to infrastructure constraints in railway transportation system, each disruption can have a very important influence on the system operation. Sometimes it is possible to affect the disrupted traffic by dispatching actions. Traffic reconfiguration may lead to a quick system recovery, but on the other hand it may lead also to cascade failures and safety infringement. The examination of a recovery strategy is hard to perform due to parameters that should be taken into account. The structure of timetable is a key factor, that has effect on the transportation system robustness. A large number of possible cases of train scheduling in the timetable makes it currently impossible to deal with this factor. Therefore, the goal of the paper is to show a method which supports the dispatching decision making process. The paper starts with a literature review, that includes the decision making process and the system recovery. Afterwards, important indicators of correct train traffic were identified, such as: number of delayed trains, average train delay, average stop loses and the probability of further disruptions. Due to the challenge to combine the indicators, the fuzzy logic was chosen to build an evaluation method. The membership functions and the decision rules were established together with experts from the railway industry. A case study was performed to show the usability of the method. Finally, a discussion about the further research about the analyzed topic was carried out.

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Correspondence to Franciszek J. Restel .

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Friedrich, J., Restel, F.J. (2020). A Fuzzy Approach for Evaluation of Reconfiguration Actions After Unwanted Events in the Railway System. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Engineering in Dependability of Computer Systems and Networks. DepCoS-RELCOMEX 2019. Advances in Intelligent Systems and Computing, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-030-19501-4_19

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