World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
67
Citations
14918
World Ranking
1330
National Ranking
258

Shuaian Wang 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 Shuaian Wang 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: 433 publications — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Shuaian Wang 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 Shuaian Wang 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: 67 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Shuaian Wang is affiliated with Hong Kong Polytechnic University in China and primarily works in the field of Engineering. Their research spans various subfields including Industrial and Manufacturing Engineering, Environmental Engineering, Ocean Engineering, Automotive Engineering, and Transportation.

Their recent papers cover several dimensions of transportation and maritime studies, addressing issues like fuel consumption, electric vehicle infrastructure, and fleet management. Notable publications include:

  • Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship (2020), published in Transportation Research Part E Logistics and Transportation Review
  • Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile (2021), published in Transportation Research Part E Logistics and Transportation Review
  • Mitigate the range anxiety: Siting battery charging stations for electric vehicle drivers (2020), published in Transportation Research Part C Emerging Technologies
  • Green technology adoption for fleet deployment in a shipping network (2020), published in Transportation Research Part B Methodological
  • Data analytics for fuel consumption management in maritime transportation: Status and perspectives (2021), published in Transportation Research Part E Logistics and Transportation Review

The main topics of Shuaian Wang's work exhibit a focus on maritime and transportation challenges, including:

  • Maritime Transport Emissions and Efficiency
  • Maritime Ports and Logistics
  • Maritime Navigation and Safety
  • Vehicle Routing Optimization Methods
  • Transportation Planning and Optimization
  • Transportation and Mobility Innovations
  • Vehicle emissions and performance

Collaboration is a significant aspect of their research, with frequent co-authors including Ran Yan, Lu Zhen, Xuecheng Tian, Min Xu, and Dan Zhuge. These partnerships span multiple papers and contribute to the breadth of their scientific output.

Key venues for their published work include:

  • SSRN Electronic Journal
  • Transportation Research Part B Methodological
  • Mathematics
  • Transportation Research Part E Logistics and Transportation Review
  • Transportation Research Part C Emerging Technologies

In addition to journal articles, Shuaian Wang has contributed to academic book publications. One example is the book titled Applications of Machine Learning and Data Analytics Models in Maritime Transportation, published in 2022 by the Institution of Engineering and Technology.

Best Publications

  • Sailing speed optimization for container ships in a liner shipping network

    Shuaian Wang;Qiang Meng

  • Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions

    Qiang Meng;Shuaian Wang;Henrik Andersson;Kristian Thun

  • How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

    Dong Yang;Lingxiao Wu;Shuaian Wang;Haiying Jia

  • Liner shipping service network design with empty container repositioning

    Qiang Meng;Shuaian Wang

  • Liner ship route schedule design with sea contingency time and port time uncertainty

    Shuaian Wang;Qiang Meng

  • Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption

    Junliang He;Youfang Huang;Wei Yan;Shuaian Wang

  • Global optimization methods for the discrete network design problem

    Shuaian Wang;Qiang Meng;Hai Yang

  • On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models

    Xiaobo Qu;Shuaian Wang;Jin Zhang

  • A two-phase optimization model for the demand-responsive customized bus network design

    Di Huang;Yu Gu;Shuaian Wang;Zhiyuan Liu

  • Liner Ship Fleet Deployment with Container Transshipment Operations

    Shuaian Wang;Qiang Meng

  • Bunker consumption optimization methods in shipping: A critical review and extensions

    Shuaian Wang;Qiang Meng;Zhiyuan Liu

  • Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand

    Qiang Meng;Tingsong Wang;Shuaian Wang

  • Robust schedule design for liner shipping services

    Shuaian Wang;Qiang Meng

  • Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship

    Ran Yan;Shuaian Wang;Yuquan Du

  • On the Stochastic Fundamental Diagram for Freeway Traffic: Model Development, Analytical Properties, Validation, and Extensive Applications

    Xiaobo Qu;Jin Zhang;Shuaian Wang

  • Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile

    Le Zhang;Le Zhang;Shuaian Wang;Xiaobo Qu

  • A tree-structured crash surrogate measure for freeways

    Yan Kuang;Xiaobo Qu;Shuaian Wang

  • Robust optimization model of schedule design for a fixed bus route

    Yadan Yan;Yadan Yan;Qiang Meng;Shuaian Wang;Xiucheng Guo

  • Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data

    Yuquan Du;Qiang Meng;Shuaian Wang;Haibo Kuang

  • Mitigate the range anxiety: Siting battery charging stations for electric vehicle drivers

    Min Xu;Hai Yang;Shuaian Wang

  • Optimal Distance Tolls under Congestion Pricing and Continuously Distributed Value of Time

    Qiang Meng;Zhiyuan Liu;Shuaian Wang

  • Speed-based toll design for cordon-based congestion pricing scheme

    Zhiyuan Liu;Qiang Meng;Shuaian Wang

  • Optimal operating strategy for a long-haul liner service route

    Qiang Meng;Shuaian Wang

Frequent Co-Authors

Qiang Meng
Qiang Meng National University of Singapore
Zhiyuan Liu
Zhiyuan Liu Southeast University
Xiaobo Qu
Xiaobo Qu Xiamen University
Lu Zhen
Lu Zhen Shanghai University
Gilbert Laporte
Gilbert Laporte HEC Montréal
Hai Yang
Hai Yang Hong Kong University of Science and Technology
Chung Yee Lee
Chung Yee Lee Hong Kong University of Science and Technology
Michael G.H. Bell
Michael G.H. Bell University of Sydney
Harilaos N. Psaraftis
Harilaos N. Psaraftis Technical University of Denmark
Kjetil Fagerholt
Kjetil Fagerholt Norwegian University of Science and Technology

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