Ying Ji

1.8k total citations
113 papers, 1.3k citations indexed

About

Ying Ji is a scholar working on Management Science and Operations Research, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Ying Ji has authored 113 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Management Science and Operations Research, 21 papers in Control and Systems Engineering and 21 papers in Computational Theory and Mathematics. Recurrent topics in Ying Ji's work include Multi-Criteria Decision Making (33 papers), Risk and Portfolio Optimization (15 papers) and Supply Chain and Inventory Management (13 papers). Ying Ji is often cited by papers focused on Multi-Criteria Decision Making (33 papers), Risk and Portfolio Optimization (15 papers) and Supply Chain and Inventory Management (13 papers). Ying Ji collaborates with scholars based in China, Singapore and Canada. Ying Ji's co-authors include Shaojian Qu, Kecun Zhang, Zaiwu Gong, Mark Goh, Hui Yang, Huanhuan Li, Zhong Wu, Hassan Raza, Lun Wang and Yefan Han and has published in prestigious journals such as Advanced Materials, PLoS ONE and Journal of Materials Chemistry A.

In The Last Decade

Ying Ji

108 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ying Ji China 21 408 204 199 194 165 113 1.3k
Shaojian Qu China 23 523 1.3× 258 1.3× 364 1.8× 289 1.5× 262 1.6× 127 1.7k
E.M.T. Hendrix Spain 21 136 0.3× 140 0.7× 172 0.9× 209 1.1× 182 1.1× 124 1.5k
Davide La Torre Italy 22 364 0.9× 120 0.6× 238 1.2× 209 1.1× 309 1.9× 188 2.0k
Han-Lin Li Taiwan 19 369 0.9× 141 0.7× 196 1.0× 250 1.3× 424 2.6× 63 1.5k
Frederic H. Murphy United States 25 485 1.2× 199 1.0× 211 1.1× 120 0.6× 228 1.4× 129 2.3k
Chang-Sheng Lin China 14 629 1.5× 214 1.0× 126 0.6× 83 0.4× 188 1.1× 49 1.3k
Francisco Ruiz Spain 22 511 1.3× 209 1.0× 312 1.6× 145 0.7× 340 2.1× 89 1.8k
Antonie Stam United States 24 566 1.4× 295 1.4× 100 0.5× 191 1.0× 238 1.4× 60 1.5k
A. Hadi‐Vencheh Iran 27 1.3k 3.3× 151 0.7× 138 0.7× 250 1.3× 430 2.6× 100 2.1k
Pankaj Gupta India 28 1.5k 3.7× 233 1.1× 274 1.4× 177 0.9× 646 3.9× 119 2.1k

Countries citing papers authored by Ying Ji

Since Specialization
Citations

This map shows the geographic impact of Ying Ji's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ying Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying Ji more than expected).

Fields of papers citing papers by Ying Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ying Ji. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ying Ji. The network helps show where Ying Ji may publish in the future.

Co-authorship network of co-authors of Ying Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Ying Ji. A scholar is included among the top collaborators of Ying Ji based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ying Ji. Ying Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ji, Ying & Fang Wu. (2025). A life cycle model of IoT for higher education based on student and market needs. Discover Computing. 28(1).
2.
Han, Yefan, et al.. (2025). ELICIT trust-driven robust consensus model in social network linguistic large-scale group decision-making. Expert Systems with Applications. 293. 129053–129053. 1 indexed citations
3.
Ji, Ying, et al.. (2024). Multi-objective mixed integer programming modelling for closed-loop supply chain network design: an enhanced Benders decomposition algorithm. Engineering Optimization. 56(12). 2478–2521. 2 indexed citations
4.
Han, Yefan, et al.. (2024). ELICIT information-based robust large-scale minimum cost consensus model under social networks. Applied Soft Computing. 170. 112647–112647. 3 indexed citations
5.
Ji, Ying, et al.. (2024). Robust maximum expert consensus model with adjustment path under uncertain environment. Applied Soft Computing. 155. 111430–111430. 6 indexed citations
6.
Qu, Shaojian, et al.. (2024). Two-Stage Distributionally Robust Minimum Cost Consensus Modeling With Loss Aversion. IEEE Transactions on Computational Social Systems. 12(5). 2084–2096. 1 indexed citations
7.
Qu, Shaojian, et al.. (2024). Multi-criteria group consensus model with risk aversion and minimum adjustment. Expert Systems with Applications. 266. 125989–125989. 5 indexed citations
8.
Qu, Shaojian, et al.. (2023). Two-Stage Robust Programming Modeling for Continuous Berth Allocation with Uncertain Vessel Arrival Time. Sustainability. 15(13). 10560–10560. 6 indexed citations
9.
Qu, Shaojian & Ying Ji. (2023). Sustainable Supply Chain Management and Optimization. Sustainability. 15(4). 3844–3844. 1 indexed citations
10.
Qu, Shaojian, et al.. (2023). The Strategic Weight Manipulation Model in Uncertain Environment: A Robust Risk Optimization Approach. Systems. 11(3). 151–151. 1 indexed citations
11.
Qu, Shaojian, et al.. (2023). Online-Review-Driven Products Ranking: A Hybrid Approach. Systems. 11(3). 148–148. 5 indexed citations
12.
Qu, Shaojian, et al.. (2022). Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China. Sustainability. 14(20). 13318–13318. 18 indexed citations
13.
Ji, Ying, et al.. (2022). Product competitiveness analysis from the perspective of customer perceived helpfulness: a novel method of information fusion research. Data Technologies and Applications. 57(4). 437–464. 8 indexed citations
14.
Han, Yefan, Ying Ji, & Shaojian Qu. (2021). A Robust Minimum-Cost Consensus Model With Uncertain Aggregation Weights Based on Data-Driven Method. IEEE Transactions on Computational Social Systems. 9(4). 1167–1184. 16 indexed citations
15.
Qu, Shaojian, et al.. (2020). The Right Time for Crowd Communication during Campaigns for Sustainable Success of Crowdfunding: Evidence from Kickstarter Platform. Sustainability. 12(18). 7642–7642. 20 indexed citations
16.
Ji, Ying, et al.. (2020). A Fuzzy Robust Weighted Approach for Multi-Criteria Bilevel Games. IEEE Transactions on Industrial Informatics. 16(8). 5369–5376. 30 indexed citations
17.
Raza, Hassan, Ying Ji, & Shaojian Qu. (2020). On Mixed Metric Dimension of Some Path Related Graphs. IEEE Access. 8. 188146–188153. 9 indexed citations
18.
Ji, Ying, et al.. (2018). Dynamic Strategies on Firm Production and Platform Advertisement in Crowdfunding considering Investor’s Perception. Discrete Dynamics in Nature and Society. 2018. 1–12. 3 indexed citations
19.
Ji, Ying, Shaojian Qu, & Zhensheng Yu. (2017). A New Method for Solving Multiobjective Bilevel Programs. Discrete Dynamics in Nature and Society. 2017. 1–10. 3 indexed citations
20.
Ji, Ying & Shaojian Qu. (2017). Proximal Point Algorithms for Vector DC Programming with Applications to Probabilistic Lot Sizing with Service Levels. Discrete Dynamics in Nature and Society. 2017. 1–8. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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