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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
  3. Altmetric Badge
    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?
  19. Altmetric Badge
    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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
2 news outlets
twitter
4 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
65 Mendeley
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Title
Robust and Online Large-Scale Optimization
Published by
ADS, January 2009
DOI 10.1007/978-3-642-05465-5
ISBNs
978-3-64-205464-8, 978-3-64-205465-5
Editors

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

X Demographics

X Demographics

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

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 > Bachelor 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

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
#1,405,202
of 22,844,985 outputs
Outputs from ADS
#680
of 37,376 outputs
Outputs of similar age
#5,800
of 169,340 outputs
Outputs of similar age from ADS
#21
of 900 outputs
Altmetric has tracked 22,844,985 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 37,376 research outputs from this source. They receive a mean Attention Score of 4.6. 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 169,340 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 96% of its contemporaries.
We're also able to compare this research output to 900 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.