Crew Planning for Commuter Rail Operations, a Case Study on Mumbai, India

Naman Kasliwal (), Sudarshan Pulapadi, Madhu N. Belur, Narayan Rangaraj, Suhani Mishra, Shamit Monga, Abhishek Singh, S. G. Sagar, P. K. Majumdar and M. K. Jagesh
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Naman Kasliwal: Indian Institute of Technology Bombay
Sudarshan Pulapadi: Indian Institute of Technology Bombay
Madhu N. Belur: Indian Institute of Technology Bombay
Narayan Rangaraj: Indian Institute of Technology Bombay
Suhani Mishra: Western Railways
Shamit Monga: Western Railways
Abhishek Singh: Western Railways
S. G. Sagar: Western Railways
P. K. Majumdar: Western Railways
M. K. Jagesh: Western Railways

A chapter in Operations Research Proceedings 2019, 2020, pp 813-819 from Springer

Abstract: Abstract We consider the problem of constructing crew duties for a large, real instance of operations for commuter train services in Mumbai, India. Optimized allotment of crew duties and enforcement of work rules ensures adequate safety and welfare of rail workers. Currently, within Indian railways, decisions related to crew allotment are made manually. The main objective is to use as few crew members as possible to execute upon the timetable. This improves the efficiency of the system by increasing the average working hours of work per duty. We also have several other secondary objectives. The presence of a large number of operational constraints makes the problem difficult to solve. Computational experiments are performed over the current train timetables and the results of our algorithm compare very favorably with the crew duty schedules in use. For the Western Railways train timetable of 2017–18, the crew duty sets required to perform the timetable was 382. The proposed algorithm achieves crew allotment with 368 sets, promising significant savings of manpower and money.

Keywords: Railways; Optimization; Crew schedule problem; Crew allotment; Duty preparation; Operations research; Constraint modelling; Metaheuristic; Resource allocation; Work load balancing (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-48439-2_99

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