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Integrated optimization of train scheduling and maintenance planning on high-speed railway corridors
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
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2019 (English)In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 87, p. 86-104Article in journal (Refereed) Published
Abstract [en]

Regular maintenances on high-speed railway facilities are performed in every night in China, and during regular maintenances, high-speed railway is not available for the sunset-departure and sunrise-arrival trains (SDSA-trains). In order to reduce the influence of regular maintenances on SDSA-trains, three operation modes are used in practice, which mainly consist of route selections between high-speed railway and normal-speed railway. In this paper, we use some linearization techniques to formulate a mixed integer linear programming (MILP) model to identify the operation modes and the timetable of SDSA-trains, by integrating the time window selection of regular maintenances on high-speed railways. The objective of the model is to minimize the total travel time of SDSA-trains. In the formulation of the model, we introduce state variables to indicate whether a train is running on high-speed railway or not, which makes it conveniently express the selection of operation modes. Based on the real data of Beijing-Guangzhou high-speed and normal-speed railway corridors in China, numerical experiments are carried out to test the proposed model and optimization method.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 87, p. 86-104
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-70744DOI: 10.1016/j.omega.2018.08.005ISI: 000474319000008Scopus ID: 2-s2.0-85052095984OAI: oai:DiVA.org:ltu-70744DiVA, id: diva2:1245089
Note

Validerad;2019;Nivå 2;2019-07-10 (johcin)

Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2019-08-16Bibliographically approved

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Kumar, Uday

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