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Indian Railways Allocating 200 rails in 5 Seconds instead of usual 5 Man-Hours

How Vishal worked with Indian Railways Petroleum Director to identify solution that will optimally allocate trains daily based on outstanding orders

Company


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Indian Railways
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(IR) is a governmental entity under the Ministry of Railways which operates India's national railway system. It is run by the government as a public good and manages the fourth largest railway network in the world by size, with a route length of 68,155 km as of March 2019.
Number of employees: 13,08,000 (2016-2017)
Headquarters: New Delhi
Revenue: 1.97 lakh crores INR (US$28 billion, 2018–2019)

Problem

One of the main source of revenue of IR is through transport of materials and goods from one place to another through rail network. IR transports coals, minerals, oil and earns a revenue for the same.
To transport petro-chemicals IR has about 200 special trains to carry the load. These trains are then manually allocated based on outstanding orders from refineries.
IR was spending a large amount of time to manually allocate the trains and it was a herculean task which took away at least 5-6 man-hours daily.
The petroleum director in 2013 was looking for a solution to reduce this time and use technology to reduce the time so that time can be invested in other activities and have a more optimal solution

Breakthrough Solution

IR approached Prof Narayan Rangaraj, IIT Bombay with this as a project. Vishal was a student under Prof Narayan Rangaraj and got this project to work on.
Vishal started with understanding the data and identifying the constraints that can help us in identify the optimal solution
One of the important metrics was to optimise the service time. We wanted the trains to arrive at the exact moment when the order was supposed to be picked up, so this involved time prediction, route optimisation and appropriate type of vehicle detection
Keeping the various parameters in mind, the solution was developed that matches the outstanding orders with the train that will service the order in order that minimises the time between an order due date and train arrival time.

Impact

The impact was multi-fold.
We were able to reduce the decision making time from 5 man-hours per day to 5 seconds, with the guaranteed optimal solution
IIT Bombay got more projects the following year with an extension to current project I worked on.
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