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Robust and Online Large-Scale Optimization

Overview of attention for book
Cover of 'Robust and Online Large-Scale Optimization'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 The Concept of Recoverable Robustness, Linear Programming Recovery, and Railway Applications
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    Chapter 2 Recoverable Robustness in Shunting and Timetabling
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    Chapter 3 Light Robustness
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    Chapter 4 Incentive-Compatible Robust Line Planning
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    Chapter 5 A Bicriteria Approach for Robust Timetabling
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    Chapter 6 Meta-heuristic and Constraint-Based Approaches for Single-Line Railway Timetabling
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    Chapter 7 Engineering Time-Expanded Graphs for Faster Timetable Information
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    Chapter 8 Time-Dependent Route Planning
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    Chapter 9 The Exact Subgraph Recoverable Robust Shortest Path Problem
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    Chapter 10 Efficient Timetable Information in the Presence of Delays
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    Chapter 11 Integrating Robust Railway Network Design and Line Planning under Failures
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    Chapter 12 Effective Allocation of Fleet Frequencies by Reducing Intermediate Stops and Short Turning in Transit Systems
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    Chapter 13 Shunting for Dummies: An Introductory Algorithmic Survey
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    Chapter 14 Integrated Gate and Bus Assignment at Amsterdam Airport Schiphol
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    Chapter 15 Mining Railway Delay Dependencies in Large-Scale Real-World Delay Data
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    Chapter 16 Rescheduling Dense Train Traffic over Complex Station Interlocking Areas
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    Chapter 17 Online Train Disposition: To Wait or Not to Wait?
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    Chapter 18 Disruption Management in Passenger Railway Transportation
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

2 news outlets
4 tweeters
1 Wikipedia page


18 Dimensions

Readers on

65 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Robust and Online Large-Scale Optimization
Published by
ADS, January 2009
DOI 10.1007/978-3-642-05465-5
978-3-64-205464-8, 978-3-64-205465-5

Ravindra K. Ahuja, Rolf H. Möhring, Christos Zaroliagis

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
China 1 2%
Germany 1 2%
Unknown 62 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 42%
Student > Master 10 15%
Student > Doctoral Student 6 9%
Researcher 5 8%
Student > Postgraduate 3 5%
Other 8 12%
Unknown 6 9%
Readers by discipline Count As %
Engineering 31 48%
Computer Science 7 11%
Business, Management and Accounting 5 8%
Mathematics 4 6%
Psychology 2 3%
Other 7 11%
Unknown 9 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2018.
All research outputs
of 13,458,177 outputs
Outputs from ADS
of 25,261 outputs
Outputs of similar age
of 340,848 outputs
Outputs of similar age from ADS
of 352 outputs
Altmetric has tracked 13,458,177 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,261 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,848 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 352 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.