Yuan-Yuan Lu

533 total citations
32 papers, 457 citations indexed

About

Yuan-Yuan Lu is a scholar working on Industrial and Manufacturing Engineering, Computer Networks and Communications and Strategy and Management. According to data from OpenAlex, Yuan-Yuan Lu has authored 32 papers receiving a total of 457 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Industrial and Manufacturing Engineering, 14 papers in Computer Networks and Communications and 3 papers in Strategy and Management. Recurrent topics in Yuan-Yuan Lu's work include Scheduling and Optimization Algorithms (24 papers), Advanced Manufacturing and Logistics Optimization (23 papers) and Optimization and Search Problems (14 papers). Yuan-Yuan Lu is often cited by papers focused on Scheduling and Optimization Algorithms (24 papers), Advanced Manufacturing and Logistics Optimization (23 papers) and Optimization and Search Problems (14 papers). Yuan-Yuan Lu collaborates with scholars based in China and Hong Kong. Yuan-Yuan Lu's co-authors include Ji‐Bo Wang, Ping Ji, Jianjun Wang, Gang Li, Feng Liu, Jing Yang, Jiayu Liu, Mengqi Liu, Fu‐Ping Gao and Cai-Min Wei and has published in prestigious journals such as Expert Systems with Applications, Journal of the Operational Research Society and International Journal of Production Research.

In The Last Decade

Yuan-Yuan Lu

31 papers receiving 441 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuan-Yuan Lu China 14 409 242 24 21 14 32 457
Chunrong Zuo China 3 298 0.7× 100 0.4× 31 1.3× 14 0.7× 36 2.6× 8 372
Jianyou Xu China 11 320 0.8× 109 0.5× 21 0.9× 28 1.3× 45 3.2× 25 348
Gokalp Yildiz Türkiye 8 321 0.8× 44 0.2× 16 0.7× 20 1.0× 29 2.1× 11 367
Ling‐Huey Su Taiwan 11 378 0.9× 101 0.4× 25 1.0× 19 0.9× 23 1.6× 31 405
Assaf Sarig Israel 12 348 0.9× 146 0.6× 38 1.6× 10 0.5× 10 0.7× 22 358
Svetlana A. Kravchenko Belarus 15 566 1.4× 324 1.3× 13 0.5× 7 0.3× 19 1.4× 35 606
Surya Danusaputro Liman United States 9 382 0.9× 179 0.7× 59 2.5× 14 0.7× 13 0.9× 18 403
Xue Huang China 17 677 1.7× 342 1.4× 40 1.7× 7 0.3× 27 1.9× 34 687
Daniel Oron Australia 18 697 1.7× 384 1.6× 78 3.3× 22 1.0× 20 1.4× 43 718
Davide Anghinolfi Italy 7 266 0.7× 67 0.3× 36 1.5× 40 1.9× 71 5.1× 10 308

Countries citing papers authored by Yuan-Yuan Lu

Since Specialization
Citations

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

Fields of papers citing papers by Yuan-Yuan Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuan-Yuan Lu. 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 Yuan-Yuan Lu. The network helps show where Yuan-Yuan Lu may publish in the future.

Co-authorship network of co-authors of Yuan-Yuan Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Yuan-Yuan Lu. A scholar is included among the top collaborators of Yuan-Yuan Lu 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 Yuan-Yuan Lu. Yuan-Yuan Lu 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.
Liu, Peng & Yuan-Yuan Lu. (2025). You are entitled to access the full text of this documentInteracting with the e-tailer’s service investment in the presence of a store brand: Selling model choice. International Journal of Industrial Engineering Computations. 16(3). 499–510. 1 indexed citations
2.
Sun, Yu, et al.. (2025). Single-machine setup time scheduling with general linear deterioration subject to makespan and total completion time minimization. Journal of the Operational Research Society. 1–16. 1 indexed citations
3.
Lv, Dan‐Yang, et al.. (2024). Scheduling with Group Technology, Resource Allocation, and Learning Effect Simultaneously. Mathematics. 12(7). 1029–1029. 11 indexed citations
4.
Lu, Yuan-Yuan, et al.. (2023). Company data sharing, product innovation and competitive strategies. Expert Systems with Applications. 234. 121083–121083. 13 indexed citations
5.
Lv, Dan‐Yang, et al.. (2023). Delivery Times Scheduling with Deterioration Effects in Due Window Assignment Environments. Mathematics. 11(18). 3983–3983. 14 indexed citations
6.
He, Hongyu, et al.. (2023). Study on Scheduling Problems with Learning Effects and Past Sequence Delivery Times. Mathematics. 11(19). 4135–4135. 1 indexed citations
7.
Lu, Yuan-Yuan, Tingting Wang, Ruiqi Wang, & Li Yang. (2020). A note on due-date assignment scheduling with job-dependent learning effects and convex resource allocation. Engineering Optimization. 53(7). 1273–1281. 13 indexed citations
8.
Lu, Yuan-Yuan, et al.. (2019). Firm heterogeneity, internal control and corporate innovation performance. 40(5). 134. 2 indexed citations
9.
Jiang, Chong, et al.. (2018). Single-machine scheduling with simultaneous considerations of resource allocation and deteriorating jobs. The Computer Journal. 62(1). 81–89. 3 indexed citations
10.
Sun, Linhui, et al.. (2018). Permutation flowshop scheduling with simple linear deterioration. Engineering Optimization. 51(8). 1281–1300. 10 indexed citations
11.
Liu, Zhusong, Zhenyou Wang, & Yuan-Yuan Lu. (2017). A Bicriteria Approach for Single Machine Scheduling with Resource Allocation, Learning Effect and a Deteriorating Maintenance Activity. Asia Pacific Journal of Operational Research. 34(4). 1750011–1750011. 4 indexed citations
12.
Wang, Ji‐Bo, Ji‐Bo Wang, Xin-Na Geng, et al.. (2017). Single Machine CON/SLK Due Date Assignment Scheduling with Controllable Processing Time and Job-dependent Learning Effects. The Computer Journal. 61(9). 1329–1337. 17 indexed citations
13.
Gao, Fu‐Ping, Mengqi Liu, Jianjun Wang, & Yuan-Yuan Lu. (2017). No-wait two-machine permutation flow shop scheduling problem with learning effect, common due date and controllable job processing times. International Journal of Production Research. 56(6). 2361–2369. 37 indexed citations
14.
Lu, Yuan-Yuan. (2015). Research on no-idle permutation flowshop scheduling with time-dependent learning effect and deteriorating jobs. Applied Mathematical Modelling. 40(4). 3447–3450. 21 indexed citations
15.
Lu, Yuan-Yuan, Jian Jin, Ping Ji, & Ji‐Bo Wang. (2015). Resource-dependent scheduling with deteriorating jobs and learning effects on unrelated parallel machine. Neural Computing and Applications. 27(7). 1993–2000. 19 indexed citations
16.
Lu, Yuan-Yuan, Jianjun Wang, & Xue Huang. (2014). Scheduling jobs with position and sum-of-processing-time based processing times. Applied Mathematical Modelling. 39(14). 4013–4021. 9 indexed citations
17.
Lu, Yuan-Yuan & Ji‐Bo Wang. (2013). Some single-machine scheduling with sum-of-processing-time-based and job-position-based processing times. Applied Mathematical Modelling. 37(10-11). 6695–6702. 8 indexed citations
18.
Lu, Yuan-Yuan, Cai-Min Wei, & Ji‐Bo Wang. (2012). Several single-machine scheduling problems with general learning effects. Applied Mathematical Modelling. 36(11). 5650–5656. 22 indexed citations
19.
Lu, Yuan-Yuan. (2011). Cooperative advertising strategy and order policy under retailers' competition. Journal of Shandong University. 1 indexed citations
20.

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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