Xingli Wu

2.7k total citations · 2 hit papers
67 papers, 2.2k citations indexed

About

Xingli Wu is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Xingli Wu has authored 67 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Management Science and Operations Research, 21 papers in Artificial Intelligence and 12 papers in Computational Theory and Mathematics. Recurrent topics in Xingli Wu's work include Multi-Criteria Decision Making (42 papers), Rough Sets and Fuzzy Logic (12 papers) and Quality Function Deployment in Product Design (9 papers). Xingli Wu is often cited by papers focused on Multi-Criteria Decision Making (42 papers), Rough Sets and Fuzzy Logic (12 papers) and Quality Function Deployment in Product Design (9 papers). Xingli Wu collaborates with scholars based in China, Saudi Arabia and Spain. Xingli Wu's co-authors include Huchang Liao, Francisco Herrera, Arian Hafezalkotob, Zeshui Xu, Xiaomei Mi, Xuedong Liang, Abdullah Al-Barakati, Rui Qin, Chenyuan Gao and Cheng Zhang and has published in prestigious journals such as Journal of Cleaner Production, European Journal of Operational Research and Journal of Business Research.

In The Last Decade

Xingli Wu

59 papers receiving 2.1k citations

Hit Papers

Probabilistic Linguistic MULTIMOORA: A Multicriteria Deci... 2018 2026 2020 2023 2018 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xingli Wu China 22 1.6k 609 519 323 254 67 2.2k
Zhang‐peng Tian China 25 1.6k 1.0× 487 0.8× 293 0.6× 470 1.5× 243 1.0× 41 2.1k
Ying-Ming Wang China 26 1.3k 0.8× 624 1.0× 382 0.7× 442 1.4× 141 0.6× 69 2.3k
Miłosz Kadziński Poland 33 1.8k 1.1× 832 1.4× 695 1.3× 389 1.2× 378 1.5× 97 2.9k
Salvatore Corrente Italy 24 1.2k 0.7× 442 0.7× 301 0.6× 250 0.8× 147 0.6× 53 1.8k
Cuiping Wei China 25 1.9k 1.1× 730 1.2× 670 1.3× 475 1.5× 157 0.6× 71 2.4k
Fatih Emre Boran Türkiye 17 1.9k 1.2× 556 0.9× 390 0.8× 588 1.8× 379 1.5× 43 2.5k
Arian Hafezalkotob Iran 16 1.2k 0.8× 257 0.4× 246 0.5× 346 1.1× 266 1.0× 21 1.7k
Xuanhua Xu China 27 1.6k 1.0× 851 1.4× 345 0.7× 145 0.4× 381 1.5× 91 2.6k
R. Krishankumar India 29 1.5k 0.9× 331 0.5× 274 0.5× 396 1.2× 435 1.7× 92 2.2k
Hsu-Shih Shih Taiwan 20 1.3k 0.8× 282 0.5× 380 0.7× 672 2.1× 571 2.2× 41 2.8k

Countries citing papers authored by Xingli Wu

Since Specialization
Citations

This map shows the geographic impact of Xingli Wu'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 Xingli Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xingli Wu more than expected).

Fields of papers citing papers by Xingli Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xingli Wu. 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 Xingli Wu. The network helps show where Xingli Wu may publish in the future.

Co-authorship network of co-authors of Xingli Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Xingli Wu. A scholar is included among the top collaborators of Xingli Wu 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 Xingli Wu. Xingli Wu 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.
Wu, Xingli & Ting Zhu. (2025). From data to diagnosis: A logical learning method to enhance interpretability in bipolar and major depressive disorder identification. European Journal of Operational Research. 325(2). 362–380.
2.
Liao, Huchang, et al.. (2025). An enhanced failure mode and effect analysis method based on preference disaggregation in risk analysis of intelligent wearable medical devices. Engineering Applications of Artificial Intelligence. 147. 110384–110384.
3.
4.
Wu, Xingli, et al.. (2025). Multi-objective optimization for risk mitigation of medical waste disposal reverse logistics network. Socio-Economic Planning Sciences. 102. 102322–102322.
5.
Wu, Xingli & Huchang Liao. (2025). A preference learning method to estimate consumer preferences from online reviews. Journal of Business Research. 201. 115741–115741.
6.
Liao, Huchang, et al.. (2025). Prescriptive analytics for dynamic multi-criterion decision making considering learned knowledge of alternatives. Expert Systems with Applications. 268. 126350–126350. 1 indexed citations
7.
Wu, Xingli, et al.. (2024). Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency. Computer Modeling in Engineering & Sciences. 139(3). 3125–3146.
8.
Wu, Xingli & Huchang Liao. (2024). Determining investment allocation strategies to improve consumer satisfaction based on a preference learning model. Journal of Retailing and Consumer Services. 82. 104140–104140. 2 indexed citations
9.
Wu, Xingli & Huchang Liao. (2023). A compensatory value function for modeling risk tolerance and criteria interactions in preference disaggregation. Omega. 117. 102836–102836. 16 indexed citations
10.
Lai, Han, et al.. (2023). A multi-subgroup decision-making method for design selection based on subjective reports and objective physiological index data. Applied Soft Computing. 146. 110667–110667. 2 indexed citations
11.
Wu, Xingli, Huchang Liao, & Chonghui Zhang. (2023). Importance-performance analysis to develop product/service improvement strategies through online reviews with reliability. Annals of Operations Research. 342(3). 1905–1924. 7 indexed citations
12.
Wu, Xingli, Huchang Liao, & Chonghui Zhang. (2023). Preference disaggregation analysis for sorting problems in the context of group decision-making with uncertain and inconsistent preferences. Information Fusion. 101. 102014–102014. 10 indexed citations
14.
Wu, Xingli, Huchang Liao, Edmundas Kazimieras Zavadskas, & Jurgita Antuchevičienė. (2022). A PROBABILISTIC LINGUISTIC VIKOR METHOD TO SOLVE MCDM PROBLEMS WITH INCONSISTENT CRITERIA FOR DIFFERENT ALTERNATIVES. Technological and Economic Development of Economy. 28(2). 559–580. 15 indexed citations
15.
Wu, Xingli, Huchang Liao, Benjamin Lev, & Edmundas Kazimieras Zavadskas. (2021). A Multiple Criteria Decision-Making Method With Heterogeneous Linguistic Expressions. IEEE Transactions on Engineering Management. 70(5). 1857–1870. 12 indexed citations
16.
17.
Liao, Huchang, Xingli Wu, Xuedong Liang, Jiuping Xu, & Francisco Herrera. (2018). A New Hesitant Fuzzy Linguistic ORESTE Method for Hybrid Multicriteria Decision Making. IEEE Transactions on Fuzzy Systems. 26(6). 3793–3807. 73 indexed citations
18.
Wu, Xingli, Huchang Liao, Zeshui Xu, Arian Hafezalkotob, & Francisco Herrera. (2018). Probabilistic Linguistic MULTIMOORA: A Multicriteria Decision Making Method Based on the Probabilistic Linguistic Expectation Function and the Improved Borda Rule. IEEE Transactions on Fuzzy Systems. 26(6). 3688–3702. 335 indexed citations breakdown →
20.
Wu, Xingli, et al.. (2018). Underground Mining Method Selection With the Hesitant Fuzzy Linguistic Gained and Lost Dominance Score Method. IEEE Access. 6. 66442–66458. 29 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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