Planning rolling stock maintenance: Optimization of train arrival dates at a maintenance center

Abstract

<p style='text-indent:20px;'>A railway network is an indispensable part of the public transportation system in many major cities around the world. In order to provide a safe and reliable service, a fleet of passenger trains must undergo regular maintenance. These maintenance operations are lengthy procedures, which are planned for one year or a longer period. The planning specifies the dates of trains' arrival at the maintenance center and should take into account the uncertain duration of maintenance operations, the periods of validity of the previous maintenance, the desired number of trains in service, and the capacity of the maintenance center. The paper presents a nonlinear programming formulation of the considered problem and several optimization procedures which were compared by computational experiments using real world data. The results of these experiments indicate that the presented approach is capable to be used in real world planning process.</p>

Model development synchronized with data mining for rolling stock maintenance strategy

“…In comparison with that of an offline mode the running time is found to be 90 s in case of 5000 reported abnormalities whereas in case of M3 strategy it is found to be reduced to 55–60 s for the same number of reported cases. 20–26…”

“…In comparison with that of an offline mode the running time is found to be 90 s in case of 5000 reported abnormalities whereas in case of M3 strategy it is found to be reduced to 55-60 s for the same number of reported cases. [20][21][22][23][24][25][26] Further, the results of month-wise transaction of data from field to metro cloud indicates that M3 provides fast automated wireless decision action on comparing the M1 and M2 with the maximum duration of 60 s. Few other studies from the past indicates that the average solution time proposed by GA approach is 20.56 min for 118,640 (Reliability 19.10%) reported malfunctioning pops which indicates reliability increases with decrease in number of alerts notifications. 4 The continuous assistant information mining dependent on the remote correspondence component enhancement of the IoT framework planned in this paper has the best presentation.…”