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Topic · Industrial and Manufacturing Engineering

Optimization and Packing Problems

Optimization and Packing Problems is a research topic within Industrial and Manufacturing Engineering. Science Explorer counts 16k research works in it since 1956. 24.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the optimization of cutting and packing problems, including knapsack problems, bin packing, heuristic and metaheuristic algorithms, three-dimensional packing, rectangular packing, genetic algorithms, and strip packing. The research explores various approaches to efficiently utilize space and resources for packing objects into containers or arranging them for optimal use.

  • Cutting
  • Packing
  • Knapsack Problem
  • Bin Packing
  • Heuristic Algorithms
  • Metaheuristic Algorithms
  • Three-Dimensional Packing
  • Rectangular Packing
  • Genetic Algorithm
  • Strip Packing
Research works
16k
fractional, since 1956
In the world top 10%
4.1k
per year above
Top-10% rate
24.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+2%
the tick is no change

Which countries lead Optimization and Packing Problems research?

By volume, China and the United States publish the most (611 and 179 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 611 works
  2. 2 United States 179 works
  3. 3 Indonesia 146 works
  4. 4 Germany 115 works
  5. 5 India 105 works
  6. 6 Brazil 91 works
  7. 7 France 86 works
  8. 8 Türkiye 81 works
  9. 9 Russia 63 works
  10. 10 Italy 57 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.

China: 39.8%United States: 11.7%Indonesia: 9.5%Germany: 7.5%6 others listed: 31.5%40%largest
China611 · 39.8%United States179 · 11.7%Indonesia146 · 9.5%Germany115 · 7.5%6 others listed483 · 31.5%

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

Which institutions lead Optimization and Packing Problems research?

By volume in 2022–2025, Huazhong University of Science and Technology publishes the most Optimization and Packing Problems research, followed by Wuhan University of Technology and Shanghai Jiao Tong University.

By volume, 2022–2025

  1. 1 Huazhong University of Science and TechnologyChina 24 works
  2. 2 Wuhan University of TechnologyChina 15 works
  3. 3 Shanghai Jiao Tong UniversityChina 13 works
  4. 4 Northeastern UniversityChina 11 works
  5. 5 Centre National de la Recherche ScientifiqueFrance 10 works
  6. 6 Tsinghua UniversityChina 10 works
  7. 7 Shanghai UniversityChina 10 works
  8. 8 Southwest Jiaotong UniversityChina 9 works
  9. 9 Universidade Estadual de Campinas (UNICAMP)Brazil 9 works
  10. 10 Beijing Institute of TechnologyChina 8 works

Who are the leading researchers in Optimization and Packing Problems?

The most-cited researchers publishing on Optimization and Packing Problems include Marco Dorigo, Xin Yao and Vittorio Maniezzo.

  1. 1 Marco Dorigo Belgium 4.5k citations
  2. 2 Xin Yao United Kingdom 3.5k citations
  3. 3 Vittorio Maniezzo Italy 3.1k citations
  4. 4 N. J. A. Sloane United States 2.8k citations

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

Where is Optimization and Packing Problems research done?

The largest centres of Optimization and Packing Problems research in 2022–2025 are Beijing (China), Shanghai (China), Wuhan (China) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 96 works
  2. 2 Shanghai China 59 works
  3. 3 Wuhan China 57 works
  4. 4 Nanjing China 25 works
  5. 5 Xi'an China 23 works
  6. 6 Hangzhou China 23 works
  7. 7 Chengdu China 22 works
  8. 8 Jakarta Indonesia 21 works
  9. 9 Tokyo Japan 21 works
  10. 10 Seoul South Korea 20 works
See Optimization and Packing Problems on the map

Where is the best place to study Optimization and Packing Problems?

Among universities, judged by research, Wuhan University of Technology, Huazhong University of Science and Technology and Shanghai 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%30%mean 16.74%fractional works in this node (log) →share in the world top 10% →Wuhan University of Technology: 15, 21.4%Huazhong University of Science and Technology: 24, 19.7%Shanghai University: 10, 22.0%Tsinghua University: 10, 28.0%Shanghai Maritime University: 8, 17.2%Northeastern University: 12, 14.4%Southwest Jiaotong University: 9, 14.5%Universidade Estadual de Campinas (UNICAMP): 9, 5.6%Beijing Institute of Technology: 8, 15.1%Shanghai Jiao Tong University: 13, 9.5%Tsinghua UniversityShanghai UniversityWuhan University of …Huazhong University …
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 Wuhan University of TechnologyChina 56.721.4%8.1×15 -13.6%
2 Huazhong University of Science and TechnologyChina 54.319.7%6.5×24 -38.3%
3 Shanghai UniversityChina 47.022.0%5.7×10 +25.7%
4 Tsinghua UniversityChina 44.028.0%2.2×10 +13.6%
5 Shanghai Maritime UniversityChina 40.517.2%13.9×8 -44.6%
6 Northeastern UniversityChina 30.314.4%5.5×12 -36.7%
7 Southwest Jiaotong UniversityChina 29.914.5%5.2×9 -6.3%
8 Universidade Estadual de Campinas (UNICAMP)Brazil 27.75.6%5.0×9 +61.2%
9 Beijing Institute of TechnologyChina 20.715.1%3.0×8 -11.4%
10 Shanghai Jiao Tong UniversityChina 17.49.5%2.5×13 -40.2%

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 Optimization and Packing Problems research growing?

Output in 2018–2022 was 2% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Optimization and Packing Problems.

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.