Al-Powered Automatic Block Planning to Maximize Asset Availability for Train Operations on Indian Railways

Background: Railway maintenance for fixed infrastructure of Engineering, Traction Distribution, and Signal & Telecommunication departments is currently planned independently. Each department requests maintenance blocks/disconnections via the BDMS system. This planning process is decentralized and manual. This often leads to inefficient block utilization, poor coordination, and suboptimal scheduling,which may reduce asset availability and impact train operations. Detailed Description: Maintenance data-such as defects and overdue tasks—is maintained separately in systems like Track Management System (TMS), Signalling Maintenance & Management System (SMMS), and Traction Distribution Management System (TDMS). Meanwhile, the Control Office Application (COA) manages block corridor availability. Without integration and coordinated scheduling, maintenance blocks/disconnections are not optimally planned, resulting in asset downtime and reduced availability of fixed infrastructure for train operation.Your task is to develop an Automatic Block Planning system that integrates maintenance, defects and corridor data to generate optimized block schedules. The system should prioritize maintenance activities to minimize asset downtime and maximize the availability of critical infrastructure, ensuring uninterrupted train operations. Expected Solution: Participants should build an Al system that includes:

• 1.Integration of maintenance data (defects, overdue maintenance) from TMS, SMMS, and TDMS with corridor block and block availability as per the Train Time Table and the goods trains forecast from the Control Office.
• 2.Uses AI/ML algorithms to prioritize and schedule maintenance tasks based on criticality, urgency, and impact on asset availability.
• 3.Optimize block scheduling to maximize asset uptime by minimizing downtime and efficiently coordinating multi-department activities.
• 4.Provides block plans over multiple time horizons-weekly and monthly—to support both short-term and long-term maintenance.

The solution should transform current decentralized and manual block planning into a data-driven, coordinated process that maximizes asset availability, improves safety, and supports reliable train operations.