Thomas Hanne

2.6k total citations
134 papers, 1.4k citations indexed

About

Thomas Hanne is a scholar working on Artificial Intelligence, Industrial and Manufacturing Engineering and Management Science and Operations Research. According to data from OpenAlex, Thomas Hanne has authored 134 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Artificial Intelligence, 32 papers in Industrial and Manufacturing Engineering and 30 papers in Management Science and Operations Research. Recurrent topics in Thomas Hanne's work include Metaheuristic Optimization Algorithms Research (22 papers), Vehicle Routing Optimization Methods (20 papers) and Multi-Criteria Decision Making (12 papers). Thomas Hanne is often cited by papers focused on Metaheuristic Optimization Algorithms Research (22 papers), Vehicle Routing Optimization Methods (20 papers) and Multi-Criteria Decision Making (12 papers). Thomas Hanne collaborates with scholars based in Switzerland, Iran and Germany. Thomas Hanne's co-authors include Tomáš Gál, Theodor J. Stewart, Rolf Dornberger, Stefan Nickel, Amir Karbassi Yazdi, Varun Gupta, José María Fernández-Crehuet, Teresa Melo, Peter Wänke and Juan Carlos Osorio Gómez and has published in prestigious journals such as European Journal of Operational Research, IEEE Access and Journal of the Operational Research Society.

In The Last Decade

Thomas Hanne

120 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
Thomas Hanne Switzerland 19 373 365 275 212 195 134 1.4k
Patrick Meyer France 15 539 1.4× 391 1.1× 207 0.8× 243 1.1× 168 0.9× 67 1.4k
Pekka Malo Finland 19 326 0.9× 526 1.4× 419 1.5× 146 0.7× 390 2.0× 59 1.8k
Feng Shan China 23 285 0.8× 267 0.7× 139 0.5× 218 1.0× 159 0.8× 95 1.6k
Pranab K. Muhuri India 24 419 1.1× 735 2.0× 225 0.8× 391 1.8× 160 0.8× 114 2.3k
Hassan A. Alsattar Iraq 26 542 1.5× 518 1.4× 225 0.8× 121 0.6× 202 1.0× 53 1.9k
Jianjun Zhu China 21 714 1.9× 353 1.0× 170 0.6× 148 0.7× 145 0.7× 102 1.4k
Kuo-Ping Lin Taiwan 23 479 1.3× 469 1.3× 86 0.3× 132 0.6× 171 0.9× 89 1.7k
K. S. Ravichandran India 23 811 2.2× 354 1.0× 148 0.5× 115 0.5× 236 1.2× 105 1.7k
Van‐Nam Huynh Japan 26 664 1.8× 846 2.3× 404 1.5× 126 0.6× 222 1.1× 171 2.2k
Abhijit Gosavi United States 19 327 0.9× 474 1.3× 197 0.7× 416 2.0× 336 1.7× 79 1.9k

Countries citing papers authored by Thomas Hanne

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Hanne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Hanne

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Hanne. A scholar is included among the top collaborators of Thomas Hanne 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 Thomas Hanne. Thomas Hanne 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.
Hanne, Thomas, et al.. (2025). Large Language Models for Structured Information Processing in Construction and Facility Management. Electronics. 14(20). 4106–4106.
2.
Hanne, Thomas, et al.. (2025). Multilingual Text Summarization in Healthcare Using Pre-Trained Transformer-Based Language Models. Computers, materials & continua/Computers, materials & continua (Print). 83(1). 201–217. 1 indexed citations
3.
Hanne, Thomas, et al.. (2025). Augmenting LLMs to Securely Retrieve Information for Construction and Facility Management. Information. 16(2). 76–76. 2 indexed citations
4.
Hanne, Thomas, et al.. (2025). QUBO Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem. IEEE Access. 13. 47086–47098.
5.
Hanne, Thomas, et al.. (2024). An intelligent platform-based tool for the development of digital transformation strategies. Procedia Computer Science. 237. 344–353.
6.
Yazdi, Amir Karbassi, et al.. (2024). Small and medium-sized enterprises in emerging markets and foreign direct investment: an integrated multi-criteria decision-making approach. Applied Economics. 57(25). 3327–3344. 2 indexed citations
7.
Hanne, Thomas, et al.. (2024). Assessing Large Language Models Used for Extracting Table Information from Annual Financial Reports. Computers. 13(10). 257–257. 3 indexed citations
8.
Pillai, Rajesh G., et al.. (2024). Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering. Applied Sciences. 14(20). 9318–9318. 7 indexed citations
9.
Sahebi, Hadi, et al.. (2023). The benefits of peer-to-peer renewable energy trading and battery storage backup for local grid. Journal of Energy Storage. 63. 106970–106970. 41 indexed citations
10.
Hanne, Thomas, et al.. (2023). Effects and challenges of the COVID-19 pandemic in supply chain management: a text analytics approach. Supply Chain Forum an International Journal. 25(4). 486–503. 6 indexed citations
11.
Wänke, Peter, Amir Karbassi Yazdi, Thomas Hanne, & Yong Tan. (2023). Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks. Transportation Planning and Technology. 46(5). 573–598. 5 indexed citations
12.
Yazdi, Amir Karbassi, et al.. (2023). Assessing repair and maintenance efficiency for water suppliers: a novel hybrid USBM-FIS framework. Operations Management Research. 16(3). 1321–1342. 1 indexed citations
13.
Wolter, Jan & Thomas Hanne. (2023). Prediction of service time for home delivery services using machine learning. Soft Computing. 28(6). 5045–5056. 2 indexed citations
14.
Dornberger, Rolf, et al.. (2022). Non-fungible Token Price Prediction with Multivariate LSTM Neural Networks. 56–61. 4 indexed citations
15.
Yazdi, Amir Karbassi, et al.. (2022). How, When, & Where temporary hospitals fit in turbulent times: A hybrid MADM optimization in the middle east. Computers & Industrial Engineering. 175. 108761–108761. 4 indexed citations
16.
Roth, Stefan, et al.. (2021). Comparative analysis of tools for matching work-related skill profiles with CV data and other unstructured data. Digital Commons - University of South Florida (University of South Florida). 1 indexed citations
17.
Moulik, Soumen, et al.. (2021). Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data. Mathematical Problems in Engineering. 2021. 1–12. 2 indexed citations
18.
Dornberger, Rolf, et al.. (2018). Pathfinding Optimization when Solving the Paparazzi Problem Comparing A* and Dijkstra's Algorithm. 16–22. 8 indexed citations
19.
Hanne, Thomas, et al.. (2003). Creating a code inspection model for simulation-based decision support. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 8 indexed citations
20.
Fandel, Günter, Tomáš Gál, & Thomas Hanne. (1997). Multiple criteria decision making : proceedings of the Twelfth International Conference, Hagen (Germany). Springer eBooks. 7 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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