A Network Flow Model Approach to Determining Optimal Intervention Programs for Railway Infrastructure Networks
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Date
2018-08
Publication Type
Journal Article
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yes
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Abstract
The determination of the optimal interventions to execute on rail infrastructure networks is a challenging task, due to the many types of objects (e.g., bridges, tracks, and switches), how the objects work together to provide service, and the possible reductions in costs and service disruptions as obtained by grouping interventions. Although railway infrastructure managers are using computer systems to help them determine intervention programs, there are none that result in the highest net benefits while taking into consideration all of these aspects. This paper presents a network flow model approach that allows for determining the optimal intervention programs for railway infrastructure networks while taking into considerations different types of objects, how the objects work together to provide service, and object and object-traffic dependencies. The network flow models are formulated as mixed integer linear programs, where the optimal intervention program is found by using the simplex and branch and bound algorithms. The modelling approach is illustrated by using it to determine the optimal intervention program for a 2200 m multi-track railway line consisting of 11 track sections, 23 switches, and 39 bridges. It is shown that the proposed constrained network flow model can be used to determine the optimal intervention program within a reasonable amount of time, when compared to more traditional models and search algorithms.
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published
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Journal / series
Volume
3 (3)
Pages / Article No.
31
Publisher
MDPI
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Software
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Date collected
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Subject
railway infrastructure; intervention program; network flow model; optimisation
Organisational unit
03859 - Adey, Bryan T. / Adey, Bryan T.
02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
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Has part: https://doi.org/10.3929/ethz-b-000408911