World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
9361
World Ranking
8561
National Ranking
2360

Gerald B. Sheblé 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 Gerald B. Sheblé 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: 144 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Gerald B. Sheblé 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 Gerald B. Sheblé 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: 36 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 1998 - IEEE Fellow For contributions to the development of Auction Methods as an alternative to power system optimization methods addressing the de-regulation of the electric utility business

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Finance
  • Microeconomics

His primary areas of investigation include Electric power system, Electric power industry, Genetic algorithm, Mathematical optimization and Load management. His Electric power industry research includes themes of Bidding, Common value auction, Microeconomics, Profit and Operations research. He has included themes like Stochastic game, Lagrangian relaxation, Algorithm, Computation and Economic dispatch in his Genetic algorithm study.

His research in Economic dispatch focuses on subjects like Nonlinear programming, which are connected to Electrical network. The study incorporates disciplines such as Power system simulation and Economic forecasting in addition to Mathematical optimization. As a member of one scientific family, he mostly works in the field of Load management, focusing on Load following power plant and, on occasion, Deregulation.

His most cited work include:

  • Genetic algorithm solution of economic dispatch with valve point loading (992 citations)
  • Unit commitment literature synopsis (441 citations)
  • Refined genetic algorithm-economic dispatch example (252 citations)

What are the main themes of his work throughout his whole career to date?

Gerald B. Sheblé mainly focuses on Electric power system, Mathematical optimization, Microeconomics, Electric power industry and Electricity market. When carried out as part of a general Electric power system research project, his work on Power-flow study is frequently linked to work in Spacecraft, therefore connecting diverse disciplines of study. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Power system simulation and Economic dispatch.

His Microeconomics research focuses on subjects like Electricity, which are linked to Supply and demand, Econometrics and Valuation. His work deals with themes such as Competition, Load management, Industrial organization and Electric power, which intersect with Electric power industry. His studies in Electricity market integrate themes in fields like Market power and Energy market.

He most often published in these fields:

  • Electric power system (27.91%)
  • Mathematical optimization (22.48%)
  • Microeconomics (20.93%)

What were the highlights of his more recent work (between 2005-2018)?

  • Electricity market (13.95%)
  • Mathematical optimization (22.48%)
  • Electricity (11.63%)

In recent papers he was focusing on the following fields of study:

His main research concerns Electricity market, Mathematical optimization, Electricity, Microeconomics and Wind power. Gerald B. Sheblé interconnects Market power, Bidding, Automaton and Factor market in the investigation of issues within Electricity market. His work in the fields of Stochastic programming overlaps with other areas such as Production cost.

Gerald B. Sheblé works mostly in the field of Stochastic programming, limiting it down to concerns involving Optimization problem and, occasionally, Genetic algorithm and Evolutionary computation. In the field of Electricity, his study on Electric power industry overlaps with subjects such as Economic forces. His Common value auction study in the realm of Microeconomics interacts with subjects such as Contingency and Work.

Between 2005 and 2018, his most popular works were:

  • Comparing Hedging Methods for Wind Power: Using Pumped Storage Hydro Units vs. Options Purchasing (69 citations)
  • Valuation of switchable tariff for wind energy (38 citations)
  • Electricity market equilibrium model with resource constraint and transmission congestion (33 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Finance
  • Microeconomics

His primary areas of study are Electricity, Electricity market, Mathematical optimization, Renewable energy and Wind power. His biological study spans a wide range of topics, including Shadow price, Flow network, Market power, Nash equilibrium and Hidden Markov model. The concepts of his Electricity market study are interwoven with issues in Supply and demand, Production and Market participant.

His Mathematical optimization research incorporates elements of Bidding, Energy supply and Complete information. His study focuses on the intersection of Renewable energy and fields such as Environmental economics with connections in the field of Electric power, Feed-in tariff, Electric power industry and Valuation of options. His studies in Wind power integrate themes in fields like Photovoltaic system, Hydroelectricity, Wave power and Energy storage.

Best Publications

  • Genetic algorithm solution of economic dispatch with valve point loading

    D.C. Walters;G.B. Sheble

  • Unit commitment literature synopsis

    G.B. Sheble;G.N. Fahd

  • Refined genetic algorithm-economic dispatch example

    G.B. Sheble;K. Brittig

  • Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments

    C. K. Pang;G. B. Sheble;F. Albuyeh

  • AGC simulator for price-based operation. I. A model

    J. Kumar;Kah-Hoe Ng;G. Sheble

  • Computational Auction Mechanisms for Restructured Power Industry Operation

    Gerald B. Sheble

  • Direct load control-A profit-based load management using linear programming

    K.-H. Ng;G.B. Sheble

  • Genetic algorithm evolution of utility bidding strategies for the competitive marketplace

    C.W. Richter;G.B. Sheble

  • A profit-based unit commitment GA for the competitive environment

    C.W. Richter;G.B. Sheble

  • Application of auctions as a pricing mechanism for the interchange of electric power

    D.L. Post;S.S. Coppinger;G.B. Sheble

  • Genetic-based unit commitment algorithm

    T.T. Maifeld;G.B. Sheble

  • A new approach to AC/DC power flow

    T. Smed;G. Andersson;G.B. Sheble;L.L. Grigsby

  • Comprehensive bidding strategies with genetic programming/finite state automata

    C.W. Richter;G.B. Sheble;D. Ashlock

  • AGC simulator for price-based operation. II. Case study results

    J. Kumar;Kah-Hoe Ng;G. Sheble

  • Unit commitment by genetic algorithm and expert system

    Gerald B. Sheblé;Timothy T. Maifeld

  • Auction implementation problems using Lagrangian relaxation

    S. Dekrajangpetch;G.B. Sheble;A. Conejo

  • Solution of the unit commitment problem by the method of unit periods

    G.B. Sheble

  • Comparing Hedging Methods for Wind Power: Using Pumped Storage Hydro Units vs. Options Purchasing

    Kory W. Hedman;Gerald B. Sheble

  • Unit commitment by genetic algorithm with penalty methods and a comparison of Lagrangian search and genetic algorithm—economic dispatch example

    G.B. Sheblé;T.T. Maifeld;K. Brittig;G. Fahd

  • Artificial Intelligence Solutions to Power System Operating Problems

    R. P. Schulte;J. N. Wrubel;S. L. Larsen;G. B. Sheble

Frequent Co-Authors

R.M. Nelms
R.M. Nelms Auburn University
Kory W. Hedman
Kory W. Hedman Arizona State University
James D. McCalley
James D. McCalley Iowa State University
Manuel A. Matos
Manuel A. Matos University of Porto
Venkataramana Ajjarapu
Venkataramana Ajjarapu Iowa State University
Vijay Vittal
Vijay Vittal Arizona State University
Kit Po Wong
Kit Po Wong University of Western Australia
Peter W. Sauer
Peter W. Sauer University of Illinois at Urbana-Champaign
João Peças Lopes
João Peças Lopes University of Porto
Mariesa L. Crow
Mariesa L. Crow Missouri University of Science and Technology

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