World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
54
Citations
10927
World Ranking
3193
National Ranking
20

Chen-Fu Chien publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Chen-Fu Chien sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 285 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Chen-Fu Chien D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Chen-Fu Chien sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 54 D-Index — 68th percentile

68% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Chen-Fu Chien is affiliated with National Tsing Hua University in Taiwan and has contributed extensively to the field of Engineering, particularly focusing on Industrial and Manufacturing Engineering. Their research portfolio includes over a hundred publications, dealing with various topics related to manufacturing systems and digital transformation in industry.

The main areas of study for Chen-Fu Chien encompass the following subfields:

  • Industrial and Manufacturing Engineering
  • Management Information Systems
  • Strategy and Management
  • Electrical and Electronic Engineering
  • Management Science and Operations Research

Chen-Fu Chien's research topics include:

  • Industrial Vision Systems and Defect Detection
  • Scheduling and Optimization Algorithms
  • Manufacturing Process and Optimization
  • Digital Transformation in Industry
  • Advanced Manufacturing and Logistics Optimization
  • Sustainable Supply Chain Management
  • Supply Chain and Inventory Management

The scientist has frequently published in the following venues:

  • Computers & Industrial Engineering
  • SSRN Electronic Journal
  • IEEE Transactions on Semiconductor Manufacturing
  • Resources Conservation and Recycling
  • Applied Soft Computing

Some notable recent papers by Chen-Fu Chien include:

  • Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies, 2020, International Journal of Production Research
  • Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor, 2020, International Journal of Production Research
  • Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for Industry 3.5 smart production, 2021, Computers & Industrial Engineering

Other important research works associated with Chen-Fu Chien involve collaborative studies on digital transformation and smart production, contributing to the evolving concept of Industry 3.5.

Frequent coauthors collaborating with Chen-Fu Chien include:

  • Hsuan-An Kuo
  • Ming-Lang Tseng
  • Tran Hong Van Nguyen
  • Tzu-Yen Hong
  • Yun-Siang Lin

Best Publications

  • An AHP-based approach to ERP system selection

    Chun-Chin Wei;Chen-Fu Chien;Mao-Jiun J. Wang

  • Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry

    Chen-Fu Chien;Li-Fei Chen

  • Circular economy meets industry 4.0: Can big data drive industrial symbiosis?

    Ming-Lang Tseng;Raymond R. Tan;Anthony S.F. Chiu;Chen-Fu Chien

  • Data mining for yield enhancement in semiconductor manufacturing and an empirical study

    Chen-Fu Chien;Wen-Chih Wang;Jen-Chieh Cheng

  • A portfolio–evaluation framework for selecting R&D projects

    Chen–Fu Chien

  • Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing

    Shao-Chung Hsu;Chen-Fu Chien

  • Using Bayesian network for fault location on distribution feeder

    Chen-Fu Chien;Shi-Lin Chen;Yih-Shin Lin

  • MANUFACTURING INTELLIGENCE FOR SEMICONDUCTOR DEMAND FORECAST BASED ON TECHNOLOGY DIFFUSION AND PRODUCT LIFE CYCLE

    Chen-Fu Chien;Yun-Ju Chen;Jin-Tang Peng

  • Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing

    Thitipong Jamrus;Chen-Fu Chien;Mitsuo Gen;Kanchana Sethanan

  • Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence

    Chen-Fu Chien;Chia-Yu Hsu;Pei-Nong Chen

  • Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies

    Chen Fu Chien;Stéphane Dauzère-Pérès;Woonghee Tim Huh;Young Jae Jang

  • A DEA Study to Evaluate the Relative Efficiency and Investigate the District Reorganization of the Taiwan Power Company

    Feng-Yu Lo;Chen-Fu Chien;J.T. Lin

  • Modelling and analysis of semiconductor manufacturing in a shrinking world: challenges and successes

    Chen-Fu Chien;Stéphane Dauzère-Pérès;Hans Ehm;John W. Fowler

  • UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution

    Wenhan Fu;Chen-Fu Chien;Chen-Fu Chien

  • Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

    Chen-Fu Chien;Yun-Siang Lin;Sheng-Kai Lin

  • Rough set theory for data mining for fault diagnosis on distribution feeder

    J.-T. Peng;C.F. Chien;T.L.B. Tseng

  • An evolutionary approach to rehabilitation patient scheduling: A case study

    Chen-Fu Chien;Fang-Pin Tseng;Chien-Hung Chen

  • Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing

    Chen-Fu Chien;Li-Fei Chen

  • Using DEA to Evaluate R&D Performance of the Computers and Peripherals Firms in Taiwan

    Chin-Tai Chen;Chen-Fu Chien;Ming-Han Lin;Jung-Te Wang

  • An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing

    Chiao-Wen Liu;Chen-Fu Chien

  • A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence

    Chen-Fu Chien;Shao-Chung Hsu;Ying-Jen Chen

  • Manufacturing Intelligence to Exploit the Value of Production and Tool Data to Reduce Cycle Time

    Chung-Jen Kuo;Chen-Fu Chien;Jan-Daw Chen

Frequent Co-Authors

Mitsuo Gen
Mitsuo Gen Tokyo University of Science
John W. Fowler
John W. Fowler Arizona State University
Lars Mönch
Lars Mönch University of Hagen
Stéphane Dauzère-Pérès
Stéphane Dauzère-Pérès École des Mines de Saint-Étienne
Ming-Lang Tseng
Ming-Lang Tseng Asian University
Tae-Eog Lee
Tae-Eog Lee Korea Advanced Institute of Science and Technology
Reha Uzsoy
Reha Uzsoy North Carolina State University
Lixing Yang
Lixing Yang Beijing Jiaotong University
Raymond R. Tan
Raymond R. Tan De La Salle University
Anthony S.F. Chiu
Anthony S.F. Chiu De La Salle University

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