Cheng-shuo Ying

689 total citations
15 papers, 473 citations indexed

About

Cheng-shuo Ying is a scholar working on Transportation, Industrial and Manufacturing Engineering and Automotive Engineering. According to data from OpenAlex, Cheng-shuo Ying has authored 15 papers receiving a total of 473 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Transportation, 7 papers in Industrial and Manufacturing Engineering and 6 papers in Automotive Engineering. Recurrent topics in Cheng-shuo Ying's work include Transportation Planning and Optimization (7 papers), Railway Systems and Energy Efficiency (5 papers) and Transportation and Mobility Innovations (5 papers). Cheng-shuo Ying is often cited by papers focused on Transportation Planning and Optimization (7 papers), Railway Systems and Energy Efficiency (5 papers) and Transportation and Mobility Innovations (5 papers). Cheng-shuo Ying collaborates with scholars based in Hong Kong and China. Cheng-shuo Ying's co-authors include Andy H.F. Chow, Kwai‐Sang Chin, Hongtai Yang, Yanlai Li, Jie Xu, Yimo Yan, Yong‐Hong Kuo, Chin Pang Ho, Yihui Wang and Lijun Ma and has published in prestigious journals such as Journal of Cleaner Production, Expert Systems with Applications and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Cheng-shuo Ying

13 papers receiving 456 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Cheng-shuo Ying Hong Kong	9	204	133	108	87	83	15	473
Yolanda Hinojosa Spain	10	234 1.1×	76 0.6×	39 0.4×	87 1.0×	51 0.6×	19	441
Zhe Liang China	18	716 3.5×	174 1.3×	114 1.1×	55 0.6×	80 1.0×	48	1.0k
José Luis González–Velarde Mexico	18	447 2.2×	79 0.6×	46 0.4×	179 2.1×	77 0.9×	39	750
Gyu M. Lee South Korea	14	197 1.0×	36 0.3×	60 0.6×	56 0.6×	64 0.8×	35	487
Royce O. Bowden United States	12	342 1.7×	53 0.4×	144 1.3×	75 0.9×	53 0.6×	20	691
Sanjin Milinković Serbia	8	239 1.2×	149 1.1×	126 1.2×	60 0.7×	48 0.6×	20	489
Xin Wu China	12	129 0.6×	280 2.1×	33 0.3×	54 0.6×	136 1.6×	40	563
Joachim Arts Netherlands	12	134 0.7×	45 0.3×	93 0.9×	134 1.5×	48 0.6×	29	512
Antônio Galvão Novaes Brazil	12	195 1.0×	70 0.5×	72 0.7×	43 0.5×	107 1.3×	35	439
In‐Jae Jeong South Korea	14	276 1.4×	46 0.3×	46 0.4×	72 0.8×	75 0.9×	32	507

Countries citing papers authored by Cheng-shuo Ying

Since Specialization

Citations

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

Fields of papers citing papers by Cheng-shuo Ying

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cheng-shuo Ying. 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 Cheng-shuo Ying. The network helps show where Cheng-shuo Ying may publish in the future.

Co-authorship network of co-authors of Cheng-shuo Ying

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

All Works

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15 of 15 papers shown

Chow, Andy H.F., et al.. (2025). Robust optimization for adaptive bus service scheduling with adversarial reinforcement learning under demand uncertainties. Transportation Research Part C Emerging Technologies. 178. 105222–105222.

Chow, Andy H.F., et al.. (2025). Adaptive and flexible rail transit network service dispatching as a partially observable Markov decision process. Transportation Research Part C Emerging Technologies. 179. 105286–105286.

Ying, Cheng-shuo, Andy H.F. Chow, Yimo Yan, Yong‐Hong Kuo, & Shouyang Wang. (2024). Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach. Transportation Research Part B Methodological. 188. 103067–103067. 8 indexed citations

Deng, Yang, et al.. (2024). A proximal policy optimization approach for food delivery problem with reassignment due to order cancellation. Expert Systems with Applications. 258. 125045–125045. 3 indexed citations

Chow, Andy H.F., et al.. (2023). Adaptive scheduling of mixed bus services with flexible fleet size assignment under demand uncertainty. Transportation Research Part C Emerging Technologies. 158. 104452–104452. 4 indexed citations

Yan, Yimo, et al.. (2023). A policy gradient approach to solving dynamic assignment problem for on-site service delivery. Transportation Research Part E Logistics and Transportation Review. 178. 103260–103260. 7 indexed citations

Ying, Cheng-shuo, et al.. (2022). Multi-agent deep reinforcement learning for adaptive coordinated metro service operations with flexible train composition. Transportation Research Part B Methodological. 161. 36–59. 45 indexed citations

Yan, Yimo, et al.. (2022). Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transportation Research Part E Logistics and Transportation Review. 162. 102712–102712. 113 indexed citations

Chow, Andy H.F., et al.. (2021). Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method. Transportation Research Part E Logistics and Transportation Review. 156. 102544–102544. 14 indexed citations

10.

Ying, Cheng-shuo, Andy H.F. Chow, Yihui Wang, & Kwai‐Sang Chin. (2021). Adaptive Metro Service Schedule and Train Composition With a Proximal Policy Optimization Approach Based on Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems. 23(7). 6895–6906. 45 indexed citations

11.

Yan, Yimo, et al.. (2021). Reinforcement Learning for Logistics and Supply Chain Management: Methodologies, State of the Art, and Future Opportunities. SSRN Electronic Journal. 3 indexed citations

12.

Ma, Lijun, et al.. (2020). Matching daily home health-care demands with supply in service-sharing platforms. Transportation Research Part E Logistics and Transportation Review. 145. 102177–102177. 33 indexed citations

13.

Ying, Cheng-shuo, Andy H.F. Chow, & Kwai‐Sang Chin. (2020). An actor-critic deep reinforcement learning approach for metro train scheduling with rolling stock circulation under stochastic demand. Transportation Research Part B Methodological. 140. 210–235. 74 indexed citations

14.

Li, Yanlai, Cheng-shuo Ying, Kwai‐Sang Chin, Hongtai Yang, & Jie Xu. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production. 195. 573–584. 78 indexed citations

15.

Ying, Cheng-shuo, Yanlai Li, Kwai‐Sang Chin, Hongtai Yang, & Jie Xu. (2018). A new product development concept selection approach based on cumulative prospect theory and hybrid-information MADM. Computers & Industrial Engineering. 122. 251–261. 46 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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