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A taxonomy of railway track maintenance planning and scheduling: A review and research trends
•Developing a novel taxonomy for railway track maintenance planning and scheduling (RTMP&S) decision-making models.•Discussing the differences in planning and scheduling problems in railway maintenance.•Considering the structural characteristics of the railway track that can affect decision-maki...
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Published in: | Reliability engineering & system safety 2021-11, Vol.215, p.107827, Article 107827 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Developing a novel taxonomy for railway track maintenance planning and scheduling (RTMP&S) decision-making models.•Discussing the differences in planning and scheduling problems in railway maintenance.•Considering the structural characteristics of the railway track that can affect decision-making models.•Reviewing the attributes of maintenance management decisions in RTMP&S decision-making.•Summarising the optimisation frameworks for modelling the RTMP&S problems and the proposed solution approaches in the literature.•Discussing research trends can help researchers and practitioners to have a clear understanding of the state of the art of RTMP&S problems and future research directions.
Railway track maintenance and renewal are vital for railway safety, train punctuality, and travel comfort. Therefore, having cost-effective maintenance is critical in managing railway infrastructure assets. There has been a considerable amount of research performed on mathematical and decision support models for improving the application of railway track maintenance planning and scheduling. This article reviews the literature in decision support models for railway track maintenance planning and scheduling and transforms the results into a problem taxonomy. Furthermore, the article discusses current approaches in optimising maintenance planning and scheduling, research trends, and possible gaps in the related decision-making models.
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ISSN: | 0951-8320 1879-0836 1879-0836 |
DOI: | 10.1016/j.ress.2021.107827 |