Quantitative Models in Railway Operations Management

Narayan Rangaraj (), Swapnesh Subramanian () and Shripad Salsingikar ()
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Narayan Rangaraj: Indian Institute of Technology Bombay
Swapnesh Subramanian: Rajiv Gandhi Institute of Technology
Shripad Salsingikar: Tata Consultancy Services

Chapter Chapter 19 in Optimization Essentials, 2024, pp 575-608 from Springer

Abstract: Abstract Railway operations management involves planning and scheduling various resources to operate the railway services efficiently. The recent advancements in computing, communication, storage hardware, and algorithms help to automate the planning process and decision-making involved in railway operations management with the help of tools and techniques from Operations Research. Studies indicate that the use of such techniques can result in significant cost savings for railways. The techniques used to solve the decision problems include Integer Programming, Simulation, (Meta) Heuristics, and Reinforcement Learning. In this chapter, we cover the basics of railway operations management, and introduce various problems involved at different levels of planning where quantitative methods are applied. We explore a few problems in detail, including the problem statement, modeling, solution techniques, and validation of models. The specific problems taken up for illustrative analysis are (a) Large-scale timetabling, (b) Rolling stock circulation problem for long-distance passenger operations, and (c) Station level planning. We illustrate the representation of the timetable as a mathematical object and describe an application of simulation and optimization tools to a large-scale timetabling exercise. We provide an example of optimizing the rolling stock circulation in Indian Railways based on an integer programming model applied to a network graph. An approach for modeling the track infrastructure of a station for planning the train movement is also illustrated.

Keywords: Operations research; Railway operations management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-99-5491-9_19

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