Indian Railways has introduced an innovative analytical approach to optimize the deployment of electric and diesel locomotives running passenger trains.
Software based on a mathematical formula will be used to increase the productivity of locomotives by determining the number of locomotives required for each train.
Indian Railways has a fleet of about 3300 electric and diesel locomotive running passenger trains across the country. Locomotives are deployed to run passenger trains through cyclic schedules called locomotive links.
Until now, locomotive links are being manually prepared by the respective sixteen zones to operate the trains allocated to the zones.
Ministry of Railways, with the help of experts, has evolved and implemented the software-based decision support system to optimize the deployment of locomotives to run passenger trains at an all India level.
The decision support system utilizes a mathematical model to work out the minimum number of locomotives required to run all the passenger trains as per timetable while meeting the maintenance and operations requirements.
A pilot optimization exercise has been conducted by Transformation Cell of Railway Board, with an active participation of all the sixteen zonal railways.
This has resulted in reorganizing of locomotive links, which will save 30 diesel and 42 electric locomotives (costing about Rs.720 crores) being currently run in passenger services.
The revised links are under implementation. These 72 locomotives released from passenger operations will be utilized to run additional freight trains and earn additional revenues.
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Since timetabling of passenger trains is a dynamic process in view of periodic changes in train schedules, introduction of new trains and also the change in traction due to ongoing electrification, Ministry of Railways in Budget 2018-19 have sanctioned a project to be implemented by CRIS to institutionalize this process of locomotive link optimization using decision support system software across Indian Railways.
Who: Indian Railways
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