DocumentCode
1848972
Title
A modular model to schedule predictive railway maintenance operations
Author
Consilvio, Alice ; Di Febbraro, Angela ; Sacco, Nicola
Author_Institution
Dept. of Mech., Energy, Manage. & Transp. Eng., Univ. of Genoa, Genoa, Italy
fYear
2015
fDate
3-5 June 2015
Firstpage
426
Lastpage
433
Abstract
This paper presents a modular model for the optimal railway maintenance scheduling problem. In particular, an innovative approach to predictive railway maintenance scheduling is applied to track maintenance, also taking into account the risk assessment, according to the ISO 55000 guidelines, and the real-time track conditions. The novelty of this approach consists of the introduction of the concept of risk in railway maintenance scheduling, thus implying that the maintenance activity priorities are based on asset criticalities, such as track degradation conditions and repair costs, and the users´ unmet demand due to traffic disturbances caused by asset faults. In the paper, after a general framework description, the relevant literature is analyzed. Then, the formal problem description is given, and some experimental results are discussed, together with some indications about the future model developments.
Keywords
ISO standards; costing; maintenance engineering; railway engineering; scheduling; ISO 55000 guidelines; asset criticalities; formal problem description; innovative approach; maintenance activity priorities; predictive railway maintenance operation scheduling; real-time track conditions; repair costs; risk assessment; track degradation conditions; track maintenance; traffic disturbances; Degradation; Predictive models; Preventive maintenance; Rail transportation; Reliability; Schedules; Predictive railways maintenance; Risk-based maintenance; Scheduling maintenance;
fLanguage
English
Publisher
ieee
Conference_Titel
Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on
Conference_Location
Budapest
Print_ISBN
978-9-6331-3140-4
Type
conf
DOI
10.1109/MTITS.2015.7223290
Filename
7223290
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