Citation

Consilvio A, Di Febbraro A, Meo R, Sacco N. EURO J. Transp. Logist. 2019; 8(5): 435-465.

Copyright

(Copyright © 2019, The author(s), Publisher Elsevier Publishing and Association of European Operational Research Societies)

DOI

10.1007/s13676-018-0117-z

PMID

unavailable

Abstract

In a railway system, maintenance activities need to be continuously performed to ensure safety and continued rail operations. In this framework, while on one hand unplanned corrective maintenance activities performed when a fault is occurred are expensive and would cause low service quality, on the other hand preventive maintenance that does not consider the actual asset condition is often unnecessary and turns out to generate avoidable costs. To deal with this issue, in this paper, a risk-based decision support system to schedule the predictive maintenance activities is proposed. In such a framework, the interventions are planned by taking into account the forecast degradation state of railway assets and performed when a given threshold is reached, thus minimizing the probability of both sudden and unnecessary operations. With the end of finding the optimal scheduling of predictive maintenance, in this paper also the space-distributed aspect of railway infrastructure is considered, defining the best path and the activities assignment for each maintenance team. The scheduling model is formulated as a Mixed Integer Linear Programming (MILP) problem aiming at based on the risk minimization, according to the ISO 55000 guidelines. A matheuristic solution approach is proposed and applied to a real rail network. The relevant results show how the proposed scheduling model can use the outputs of predictive tools and degradation models, based on data from field, to mitigate the sudden failure risk by means of a cost-effective maintenance plan at a network level.

Language: en