Kwang Ryel Ryu

1.5k total citations
52 papers, 901 citations indexed

About

Kwang Ryel Ryu is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Kwang Ryel Ryu has authored 52 papers receiving a total of 901 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Industrial and Manufacturing Engineering, 13 papers in Artificial Intelligence and 9 papers in Computational Theory and Mathematics. Recurrent topics in Kwang Ryel Ryu's work include Maritime Ports and Logistics (20 papers), Advanced Manufacturing and Logistics Optimization (16 papers) and Advanced Multi-Objective Optimization Algorithms (7 papers). Kwang Ryel Ryu is often cited by papers focused on Maritime Ports and Logistics (20 papers), Advanced Manufacturing and Logistics Optimization (16 papers) and Advanced Multi-Objective Optimization Algorithms (7 papers). Kwang Ryel Ryu collaborates with scholars based in South Korea and United States. Kwang Ryel Ryu's co-authors include Taejin Park, Ri Choe, Kap Hwan Kim, Jeongmin Kim, Taejin Park, Young Hun Kim, Giltae Song, Seonghyun Kim, Su Min Jeon and Taesung Kim and has published in prestigious journals such as Energy and Buildings, International Journal of Production Economics and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Kwang Ryel Ryu

47 papers receiving 845 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kwang Ryel Ryu South Korea	17	668	254	142	139	105	52	901
Marco Caserta Germany	13	497 0.7×	134 0.5×	75 0.5×	78 0.6×	95 0.9×	28	702
Deniz Türsel Eliiyi Türkiye	15	632 0.9×	88 0.3×	52 0.4×	109 0.8×	31 0.3×	50	793
Andreas Bortfeldt Germany	22	2.1k 3.1×	177 0.7×	40 0.3×	42 0.3×	56 0.5×	39	2.2k
Pasquale Legato Italy	15	1.0k 1.5×	351 1.4×	26 0.2×	434 3.1×	105 1.0×	44	1.1k
Ning Zhao China	15	686 1.0×	80 0.3×	44 0.3×	60 0.4×	14 0.1×	80	954
Antônio Augusto Chaves Brazil	13	310 0.5×	54 0.2×	111 0.8×	50 0.4×	15 0.1×	43	485
Laura I. Burke United States	13	413 0.6×	103 0.4×	246 1.7×	26 0.2×	10 0.1×	21	706
Hossein Jula United States	11	565 0.8×	359 1.4×	18 0.1×	97 0.7×	43 0.4×	25	769
Jaap A. Ottjes Netherlands	12	363 0.5×	171 0.7×	16 0.1×	62 0.4×	48 0.5×	36	480
Shahin Gelareh France	16	897 1.3×	214 0.8×	23 0.2×	259 1.9×	16 0.2×	40	1.0k

Countries citing papers authored by Kwang Ryel Ryu

Since Specialization

Citations

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

Fields of papers citing papers by Kwang Ryel Ryu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kwang Ryel Ryu. 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 Kwang Ryel Ryu. The network helps show where Kwang Ryel Ryu may publish in the future.

Co-authorship network of co-authors of Kwang Ryel Ryu

This figure shows the co-authorship network connecting the top 25 collaborators of Kwang Ryel Ryu. A scholar is included among the top collaborators of Kwang Ryel Ryu 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 Kwang Ryel Ryu. Kwang Ryel Ryu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Kim, Jeongmin & Kwang Ryel Ryu. (2020). An RTS-based algorithm for noisy optimization by strategic sample accumulation. 45–46. 1 indexed citations

Kim, Seongwoo, Se Young Kim, & Kwang Ryel Ryu. (2018). Deep Learning Experiments with Skewed Data for Defect Prediction in Plastic Injection Molding. 1–2. 5 indexed citations

Kim, Jeongmin, et al.. (2017). Comparison Of Different K-Nn Models For Speed Prediction In An Urban Traffic Network. Zenodo (CERN European Organization for Nuclear Research). 10(2). 419–422. 2 indexed citations

Ryu, Kwang Ryel, et al.. (2015). Iterative Replanning of Diesel Generator and Energy Storage System for Stable Operation of an Isolated Microgrid. World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering. 2(2). 1 indexed citations

Kim, Seyoung, et al.. (2015). Learning a Bayesian Network for Situation-Aware Smart Home Service: A Case Study with a Robot Vacuum Cleaner. World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering. 2(2).

Kim, Jeongmin, et al.. (2014). Short-Term Prediction of Vehicle Speed on Main City Roads using the k-Nearest Neighbor Algorithm. Journal of Intelligence and Information Systems. 20(1). 121–131. 10 indexed citations

Park, Taejin, et al.. (2013). Planning for Selective Remarshaling in an Automated Container Terminal Using Coevolutionary Algorithms. International journal of industrial engineering. 20. 8 indexed citations

Choe, Ri, et al.. (2012). Real-time scheduling of twin stacking cranes in an automated container terminal using a genetic algorithm. 238–243. 10 indexed citations

Choe, Ri, et al.. (2012). Deriving a robust policy for container stacking using a noise-tolerant genetic algorithm. 31–36. 15 indexed citations

10.

Choe, Ri, et al.. (2011). Optimizing Automated Stacking Crane Dispatching Strategy Using an MOEA for an Automated Container Terminal. 216–217.

11.

Park, Taejin, et al.. (2010). Optimization of Stacking Strategies Considering Yard Occupancy Rate in an Automated Container Terminal. Jeongbo gwahaghoe nonmunji. keompyuting ui silje. 16(11). 1106–1110. 1 indexed citations

12.

Choe, Ri, et al.. (2009). Comparison of operations of AGVs and ALVs in an automated container terminal. Journal of Intelligent Manufacturing. 22(3). 413–426. 56 indexed citations

13.

Kang, Byoung‐Ho, et al.. (2006). The Operation of the Yard in a Terminal. Journal of Intelligence and Information Systems. 12(3). 47–65. 1 indexed citations

14.

Park, Taejin & Kwang Ryel Ryu. (2006). A dual-population genetic algorithm for balanced exploration and exploitation.. Computational intelligence. 90–95. 1 indexed citations

15.

Kim, Kap Hwan, Su Min Jeon, & Kwang Ryel Ryu. (2006). Deadlock prevention for automated guided vehicles in automated container terminals. OR Spectrum. 28(4). 659–679. 36 indexed citations

16.

Ryu, Kwang Ryel, et al.. (2006). Deriving stacking strategies for export containers with uncertain weight information. Journal of Intelligent Manufacturing. 17(4). 399–410. 101 indexed citations

17.

Park, Taejin & Kwang Ryel Ryu. (2006). Crew pairing optimization by a genetic algorithm with unexpressed genes. Journal of Intelligent Manufacturing. 17(4). 375–383. 17 indexed citations

18.

Kim, Kap Hwan, et al.. (2004). A beam search algorithm for the load sequencing of outbound containers in port container terminals. OR Spectrum. 26(1). 93–116. 46 indexed citations

19.

Ryu, Kwang Ryel, et al.. (1997). A Genetic Algorithm Hybrid for Hierarchical Reactive Scheduling.. 497–504. 6 indexed citations

20.

Ryu, Kwang Ryel & Keki B. Irani. (1992). Learning from goal interactions in planning: goal stack analysis and generalization. National Conference on Artificial Intelligence. 401–407. 6 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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