Overhaul Resource Planning for Rolling Stock Using MIP Models

Jiravanstit, Rinrada; Tharmmaphornphilas, Wipawee

doi:10.4186/ej.2017.21.5.145

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…MRTS is widely used due to its speed, reliability, and comfort. 1 The only Indian metro to bag an award from a world safety organization is Chennai Metro Rail Limited (CMRL). Chennai metro rail Limited (CMRL) began its service run in 2017 as a joint venture of the Government of India and the Government of Tamilnadu.…”

Section: Introductionmentioning

confidence: 99%

Model development synchronized with data mining for rolling stock maintenance strategy

Prabhakaran¹,

Anandakumar²,

Priyanka³

et al. 2022

Advances in Mechanical Engineering

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Indian metro system is comprised of many functional units for enabling smooth run of trains namely tracks, rolling stock, signaling and communication, operations and control center, projects, and design. Among those functional units, Rolling Stock (RS) is an integral part of any rapid transit system and also the most critical space if proper maintenance measures are neglected. The rolling stock department comprises interdisciplinary teams working together to ensure efficient delivery of trains on a service run. The trainsets of Metro Rail Networks are often subjected to both periodic and corrective maintenance based on service requirements. The maintenance schedule of the trainsets is monitored through a wireless communication mode. The train operator is responsible for alerting the nodal person regarding subsequent correspondence in the event of any emergency maintenance necessity. Therefore, this paper concentrates on the development of an IoT-based automatized maintenance prioritizing platform based on the incorrect operational sequence number that pops up in the operator’s cabin. A mathematical model is synchronized with the alert triggering signal from the field to categorize hierarchical decision-making on preventative and corrective maintenance. Simultaneously, a Genetic Algorithm is implemented to analyze the adopted combinations of maintenance say M1, M2, and M3 to identify the model that produced precise results. The test results reveal that the M3 model, which includes both corrective and preventative maintenance exhibits higher efficiency with a probability of 0.92%–0.98%. In addition, the combined maintenance prioritization system M3 offers the quickest analyzing time in the cloud computing platform (0.18 s) and the highest transaction performance on real-time datasets.

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Section: Introductionmentioning

confidence: 99%