Science Explorer Interactive view Map

Advanced Queuing Theory Analysis

Advanced Queuing Theory Analysis is a research topic within Management Information Systems. Science Explorer counts 20k research works in it since 1953. 19.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the operations management of call centers, with an emphasis on queueing systems, workload management, staffing optimization, patient flow, and performance analysis in heavy traffic regimes. It explores various aspects of call center operations and service systems, utilizing queueing theory and dynamic scheduling to improve efficiency and customer service.

  • Call Center
  • Queueing Systems
  • Service Systems
  • Workload Management
  • Staffing Optimization
  • Patient Flow
  • Performance Analysis
  • Queueing Theory
  • Heavy Traffic Regime
  • Dynamic Scheduling
Research works
20k
fractional, since 1953
In the world top 10%
4k
per year above
Top-10% rate
19.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-12%
the tick is no change

Which countries lead Advanced Queuing Theory Analysis research?

By volume, India and China publish the most (333 and 297 works in 2022–2025).

By volume, 2022–2025

  1. 1 India 333 works
  2. 2 China 297 works
  3. 3 United States 264 works
  4. 4 Russia 101 works
  5. 5 France 58 works
  6. 6 United Kingdom 53 works
  7. 7 Japan 47 works
  8. 8 Canada 39 works
  9. 9 Germany 38 works
  10. 10 Netherlands 37 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

India: 26.3%China: 23.4%United States: 20.9%Russia: 8.0%6 others listed: 21.4%26%largest
India333 · 26.3%China297 · 23.4%United States264 · 20.9%Russia101 · 8.0%6 others listed272 · 21.4%

Shares of the rows listed above, not of the whole node.

Which institutions lead Advanced Queuing Theory Analysis research?

By volume in 2022–2025, Vellore Institute of Technology University publishes the most Advanced Queuing Theory Analysis research, followed by Annamalai University and National Research Tomsk State University.

Who are the leading researchers in Advanced Queuing Theory Analysis?

The most-cited researchers publishing on Advanced Queuing Theory Analysis include Ian F. Akyildiz, Philip S. Yu and Peng Shi.

  1. 1 Ian F. Akyildiz United States 8.2k citations
  2. 2 Philip S. Yu United States 6.6k citations
  3. 3 Peng Shi Australia 5k citations
  4. 4 Zhu Han United States 4.7k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Advanced Queuing Theory Analysis research done?

The largest centres of Advanced Queuing Theory Analysis research in 2022–2025 are Beijing (China), Chennai (India), Moscow (Russia) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Chidambaram.

Largest cities, 2022–2025

  1. 1 Beijing China 49 works
  2. 2 Chennai India 43 works
  3. 3 Moscow Russia 41 works
  4. 4 Shanghai China 26 works
  5. 5 Vellore India 23 works
  6. 6 Nanjing China 23 works
  7. 7 Chidambaram India 22 works
  8. 8 New York United States 18 works
  9. 9 Tomsk Russia 17 works
  10. 10 London United Kingdom 16 works

Where it is the local speciality

  1. ChidambaramIN · 22.4 works63×
← less than its size predictsmore →

Location quotient: how much more of its research is in Advanced Queuing Theory Analysis than the world average.

See Advanced Queuing Theory Analysis on the map

Where is the best place to study Advanced Queuing Theory Analysis?

Among universities, judged by research, Vellore Institute of Technology University, Indian Institute of Technology Roorkee and Yanshan University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%mean 15.36%fractional works in this node (log) →share in the world top 10% →Vellore Institute of Technology University: 22, 7.8%Indian Institute of Technology Roorkee: 12, 21.3%Yanshan University: 14, 23.2%Belarusian State University: 12, 19.1%Technion – Israel Institute of Technology: 9, 23.5%National Research Tomsk State University: 16, 5.4%Carnegie Mellon University: 10, 21.8%Annamalai University: 22, 5.2%Cornell University: 12, 17.2%Eindhoven University of Technology: 12, 9.1%Yanshan UniversityIndian Institute of …Belarusian State Uni…Vellore Institute of…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Vellore Institute of Technology UniversityIndia 69.47.8%12.9×22 +858.1%
2 Indian Institute of Technology RoorkeeIndia 66.021.3%15.0×12 +56.1%
3 Yanshan UniversityChina 65.223.2%23.4×14 -51.3%
4 Belarusian State UniversityBelarus 64.319.1%70.8×12 -13.5%
5 Technion – Israel Institute of TechnologyIsrael 62.223.5%15.3×9 -48.9%
6 National Research Tomsk State UniversityRussia 60.95.4%46.2×16 +196.0%
7 Carnegie Mellon UniversityUnited States 60.521.8%13.3×10 -28.6%
8 Annamalai UniversityIndia 59.25.2%63.8×22 +103.3%
9 Cornell UniversityUnited States 55.017.2%7.0×12 +64.3%
10 Eindhoven University of TechnologyNetherlands 52.89.1%19.9×12 -25.7%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Advanced Queuing Theory Analysis research growing?

Output in 2018–2022 was 12% lower than in 2013–2017, peaking in 2002. The fastest-growing topics are Advanced Queuing Theory Analysis.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.