Prof. Dr. Thomas Hanne
Prof. Dr. Thomas Hanne
Activities at FHNW
- Head of Competence Center Systems Engineering
- Lecturer in Business Information Technology
Main Competences
- Systems Engineering
Master of Science in Business Information Systems
- Innovation Thinking Project (Practice-Oriented Research Project; Applying Research Methodologies)
- Optimization for Business Improvement (Modeling, Simulation and Optimization)
- Supply Chain Management
- Business Analytics: Quantitative Methods (Quantitative Methods for Business)
- Coaching and supervision of student projects and master’s theses
Bachelor of Science in Wirtschaftsinformatik (German)
- Logistics and Supply Chain Management
- Operations Research
- Coaching and supervision of student projects and bachelor’s theses
- Continuous and Combinatorial Optimization
- Evolutionary Algorithms and Metaheuristics
- Multiobjective Optimization and Decision Support
- Discrete-Event Simulation
- Applications in Logistics and Supply Chain Management.
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No peer reviewed content available
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Peer reviewedKhodoomi, M. R., Seif, M., & Hanne, T. (2024). Effects and challenges of the COVID-19 pandemic in supply chain management: a text analytics approach. Supply Chain Forum, 25(4), 486–503. https://doi.org/10.1080/16258312.2023.2253523
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Peer reviewedWong, K., Dornberger, R., & Hanne, T. (2024). An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks. Evolutionary Intelligence, 17, 2081–2089. https://doi.org/10.1007/s12065-022-00795-y
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Peer reviewedGatziu Grivas, S., Hanne, T., Imhof, D., Bugmann, D., & Schmitter, P. (2024). An intelligent platform-based tool for the development of digital transformation strategies. Procedia Computer Science, 237, 344–353. https://doi.org/10.1016/j.procs.2024.05.114
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Peer reviewedWanke, P. F., Yazdi, A. K., Hanne, T., & Tan, Y. (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. https://doi.org/10.1080/03081060.2023.2198517
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Ehrenthal, J., Hanne, T., Telesko, R., & Gachnang, P. (2023). Echtzeit Ressourcendisposition von Personal und Rollmaterial in der Eisenbahnbranche. Innosuisse. https://irf.fhnw.ch/handle/11654/43337
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Peer reviewedSarfaraz, A. H., Yazdi, A. K., Hanne, T., & Hosseini, R. S. (2023). Decision support for technology transfer using fuzzy quality function deployment and a fuzzy inference system. Journal of Intelligent & Fuzzy Systems, 44(5), 7995–8014. https://doi.org/10.3233/jifs-222232
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Peer reviewedAlcantara, T. H. M., Krütli, D., Ravada, R., & Hanne, T. (2023). Multilingual text summarization for German texts using transformer models. Information, 14(6), 303. https://doi.org/10.3390/info14060303
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Hanne, T., & Dornberger, R. (2023). Computational Intelligence in Logistik und Supply Chain Management. Springer Gabler. https://doi.org/10.1007/978-3-031-21452-3
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Peer reviewedGachnang, P., Ehrenthal, J., Telesko, R., & Hanne, T. (2023). Determination of weights for multiobjective combinatorial optimization in incident management with an evolutionary algorithm. IEEE Access, 11, 138502–138514. https://doi.org/10.1109/ACCESS.2023.3339128
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Peer reviewedAjripour, I., & Hanne, T. (2023). Using the fuzzy best worst method for evaluating strategic planning models. Processes, 11(4), 1284. https://doi.org/10.3390/pr11041284
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Peer reviewedLi, X., Curiger, M., Dornberger, R., & Hanne, T. (2023). Optimized Computational Diabetes Prediction with Feature Selection Algorithms. 2023 - 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. ISMSI, International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. https://irf.fhnw.ch/handle/11654/48186
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Peer reviewedSahebi, H., Khodoomi, M. R., Seif, M., Pishvaee, M., & Hanne, T. (2023). The benefits of peer-to-peer renewable energy trading and battery storage backup for local grid. Journal of Energy Storage, 63, 106970. https://doi.org/10.1016/j.est.2023.106970
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Peer reviewedWolter, J., & Hanne, T. (2023). Prediction of service time for home delivery services using machine learning. Soft Computing, 28, 5045–5056. https://doi.org/10.1007/s00500-023-09220-7
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Peer reviewedYazdi, A. K., Muneeb, F. M., Wanke, P. F., Hanne, T., & Ali, A. (2023). How, when, & where temporary hospitals fit in turbulent times: a hybrid MADM optimization in the middle east. Computers & Industrial Engineering, 175, 108761. https://doi.org/10.1016/j.cie.2022.108761
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Peer reviewedHeiniger, N., Massaro, G., Hanne, T., & Dornberger, R. (2023). Solving the nurse scheduling problem in crisis situations applying a genetic algorithm. 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI 2023), 65–71. https://doi.org/10.1109/ISCMI59957.2023.10458617
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Peer reviewedCrego, A., Hanne, T., & Dornberger, R. (2023). Multiobjective optimization of airline crew management with a genetic algorithm. In A. Abraham, A. Bajaj, N. Gandhi, A. M. Madureira, & C. Kahraman (Eds.), Innovations in Bio-Inspired Computing and Applications. Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022) Held During December 15-17, 2022 (pp. 109–119). Springer. https://doi.org/10.1007/978-3-031-27499-2_10
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Peer reviewedSchöpflin, T., Zimmerli, P., Dornberger, R., & Hanne, T. (2023). Weighted pathfinding in the paparazzi problem with dynamic obstacles. In A. Abraham, S. Pllana, G. Casalino, K. Ma, & A. Bajaj (Eds.), Intelligent Systems Design and Application. 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) (Vol. 1, pp. 85–95). Springer. https://doi.org/10.1007/978-3-031-27440-4_9
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Peer reviewedSuter, L. D., Meyer, D., Hanne, T., & Dornberger, R. (2023). A genetic algorithm considering earth curvature to plan a flight route of minimal distance. 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), 59–64. https://doi.org/10.1109/ISCMI59957.2023.10458489
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Peer reviewedMiccoli, A., Hanne, T., & Dornberger, R. (2023). Analyzing the computing time to solve single row facility layout problems by simulated annealing in a Python framework. Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 66–72. https://doi.org/10.1145/3596947.3596953
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Peer reviewedRuf, Y., Hanne, T., & Dornberger, R. (2023). Classification of brand images using convolutional neural networks. In A. Abraham, T. Hanne, N. Gandhi, P. M. Mishra, A. Bajaj, & P. Siarry (Eds.), Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) (pp. 528–539). Springer. https://doi.org/10.1007/978-3-031-27524-1_50
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Peer reviewedChintala, P. C., Dornberger, R., & Hanne, T. (2022). Robotic path planning by Q learning and a performance comparison with classical path finding algorithms. International Journal of Mechanical Engineering and Robotics Research, 11(6), 373–378. https://doi.org/10.18178/ijmerr.11.6.373-378
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Peer reviewedHanne, T., Gachnang, P., Gatziu Grivas, S., Kirecci, I., & Schmitter, P. (2022). Artificial intelligence and machine learning for maturity evaluation and model validation. ICEME 2022. The 2022 13th International Conference on E-Business, Management and Economics (ICEME 2022). Beijing, China (Vurtual Conference), July 16-18, 2022, 256–260. https://doi.org/10.1145/3556089.3556102
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Peer reviewedFix, S., Probst, T., Ruggli, O., Hanne, T., & Christen, P. (2022). Automatic programming as an open-ended evolutionary system. International Journal of Computer Information Systems and Industrial Management Applications, 14, 204–212. https://doi.org/10.26041/fhnw-7460
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Gachnang, P., Ehrenthal, J., Hanne, T., & Dornberger, R. (2022). Quantum computing in supply chain management state of the art and research directions. Asian Journal of Logistics Management, 1(1), 57–73. https://doi.org/10.14710/ajlm.2022.14325
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Peer reviewedMonajemzadeh, N., Karbassi Yazdi, A., Hanne, T., Shirbadadi, S., & Khosravi, Z. (2022). Identifying and prioritizing export-related CSFs of steel products using hybrid multi-criteria methods. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2022.2077162
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Peer reviewedSarfaraz, A. H., Yazdi, A. K., Hanne, T., Gizem, Ö., Khalili-Damghani, K., & Husseinagha, S. M. (2022). Analyzing the investment behavior in the Iranian stock exchange during the COVID-19 pandemic using hybrid DEA and data mining techniques. Mathematical Problems in Engineering. https://doi.org/10.1155/2022/1667618
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Fink, M., Morillo, L., Hanne, T., & Dornberger, R. (2022). Optimizing an inventory routing problem using a modified tabu search. In M. Saraswat, H. Sharma, K. Balachandran, J. H. Kim, & J. C. Bansal (Eds.), Congress on Intelligent Systems. Proceedings of CIS 2021, Volume 2 (pp. 577–586). Springer. https://doi.org/10.1007/978-981-16-9113-3_42
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Peer reviewedYazdi, A. K., Mehdiabadi, A., Hanne, T., Sarfaraz, A. H., & Yazdian, F. T. (2022). Evaluating the performance of oil and gas companies by an extended balanced scorecard and the hesitant fuzzy best-worst method. Mathematical Problems in Engineering, 2022(1), 19779. https://doi.org/10.1155/2022/1019779
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Peer reviewedCampos Macias-Hammel, N., Düggelin, W., Ruf, Y., & Hanne, T. (2022). Building a technology recommender system using web crawling and natural language processing technology. Algorithms, 15(8). https://doi.org/10.3390/a15080272
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Peer reviewedKarbassi Yazdi, A., Spulbar, C., Hanne, T., & Birau, R. (2022). Ranking performance indicators related to banking by using hybrid multicriteria methods in an uncertain environment: a case study for Iran under COVID-19 conditions. Systems Science & Control Engineering, 10(1), 166–180. https://doi.org/10.1080/21642583.2022.2052996
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Peer reviewedSubaskaran, A., Krähemann, M., Hanne, T., & Dornberger, R. (2022). Comparison of ant colony optimization algorithms for small-sized travelling salesman problems. In A. Abraham, A. M. Madureira, A. Kaklauskas, N. Gandhi, A. Bajaj, A. K. Muda, D. Kriksciuniene, & J. C. Ferreira (Eds.), Innovations in Bio-Inspired Computing and Applications. Proceedings of the 12th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2021) Held During December 16–18, 2021 (pp. 15–23). Springer. https://doi.org/10.1007/978-3-030-96299-9_2
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Peer reviewedBoss, M., Kunz, L., Wüthrich, J., & Hanne, T. (2022). Teaching encryption algorithms with serious games. In A. Abraham, P. Siarry, V. Piuri, N. Gandhi, G. Casalino, O. Castillo, & P. Hung (Eds.), Hybrid Intelligent Systems (pp. 495–507). Springer. https://doi.org/10.1007/978-3-030-96305-7_46
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Peer reviewedBoeh, R., Hanne, T., & Dornberger, R. (2022). A comparison of linear rank and tournament for parent selection in a genetic algorithm solving a dynamic travelling salesman problem. 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 97–102. https://doi.org/10.1109/ISCMI56532.2022.10068458
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Peer reviewedBranny, J., Dornberger, R., & Hanne, T. (2022). Non-fungible token price prediction with multivariate LSTM neural networks. 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 56–61. https://doi.org/10.1109/ISCMI56532.2022.10068442
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Peer reviewedFrey, E., Orefice, M., Hanne, T., & Dornberger, R. (2022). A genetic algorithm to solve the order picking problem in a warehouse with systematic item distribution. In A. Abraham, A. Engelbrecht, F. Scotti, N. Gandhi, P. M. Mishra, G. Fortino, V. Sakalauskas, & S. Pllana (Eds.), Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (pp. 586–597). Springer. https://doi.org/10.1007/978-3-030-96302-6_55
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Peer reviewedAnkem Venkata, H. B., Calazacon, A., Mahmoud, T., & Hanne, T. (2022). A technology recommender system based on web crawling and natural language processing. 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). https://doi.org/10.1109/AIC55036.2022.9848970
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Peer reviewedSchär, K., Schwank, P., Dornberger, R., & Hanne, T. (2022). Pathfinding in the paparazzi problem comparing different distance measures. In M. S. Uddin, P. K. Jamwal, & J. C. Bansal (Eds.), Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2021 (pp. 81–95). Spinger. https://doi.org/10.1007/978-981-19-0332-8_7
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Peer reviewedSchären, T. M., Hanne, T., & Dornberger, R. (2022). The Xoshiro+ pseudorandom number generator in a computer chess program. In A. Abraham, A. Engelbrecht, F. Scotti, N. Gandhi, P. M. Mishrai, G. Fortino, V. Sakalauskas, & S. Pllana (Eds.), Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (pp. 33–42). Springer. https://doi.org/10.1007/978-3-030-96302-6_3
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Peer reviewedSuppan, M., Hanne, T., & Dornberger, R. (2022). Ant colony optimization to solve the rescue problem as a vehicle routing problem with hard time windows. In M. S. Uddin, P. K. Jamwal, & J. C. Bansal (Eds.), Proceedings of international joint conference on advances in computational intelligence. Algorithms for intelligent systems (pp. 53–65). Springer. https://doi.org/10.1007/978-981-19-0332-8_5
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Peer reviewedMinder, S., Funken, M., Dornberger, R., & Hanne, T. (2022). Assessing the quality of car racing controllers in a virtual setting under changed conditions. ISMSI ’22: Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 73–79. https://doi.org/10.1145/3533050.3533062
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Peer reviewedJohner, R., Lanaia, A., Dornberger, R., & Hanne, T. (2022). Comparing the pathfinding algorithms A*, Dijkstra’s, Bellman-Ford, Floyd-Warshall, and best first search for the paparazzi problem. In M. Saraswat, H. Sharma, K. Balachandran, J. H. Kim, & J. C. Bansal (Eds.), Congress on Intelligent Systems. Proceedings of CIS 2021 (Vol. 2, pp. 561–576). Springer. https://doi.org/10.1007/978-981-16-9113-3_41
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Peer reviewedSaner, K., Smith, K., Hanne, T., & Dornberger, R. (2022). Optimization of artificial landscapes with a hybridized firefly algorithm. Journal of Advances in Information Technology, 13(4), 374–380. https://doi.org/10.12720/jait.13.4.374-380
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Peer reviewedMoser, L., Saner, K., Oggier, V., & Hanne, T. (2021). A serious game for teaching genetic algorithms. In K. Arai (Ed.), Proceedings of the Future Technologies Conference (FTC) 2021 (Vol. 1). https://doi.org/10.1007/978-3-030-89912-7_57
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Peer reviewedMeier, D., Keller, B., Kolb, M., & Hanne, T. (2021). Solving inventory routing problems with the Gurobi Branch-and-Cut Algorithm. In B. Dorronsoro, L. Amodeo, M. Pavone, & P. Ruiz (Eds.), Optimization and Learning. 4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021. Proceedings (pp. 173–189). Springer. https://doi.org/10.1007/978-3-030-85672-4_13
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Peer reviewedArockia, A., Lochbrunner, M., Hanne, T., & Dornberger, R. (2021). Benchmarking tabu search and simulated annealing for the capacitated vehicle routing problem. ICCMB 2021. 2021 4th International Conference on Computers in Management and Business. Singapore, January 30-February 1, 2021, 118–124. https://doi.org/10.1145/3450588.3450940
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Peer reviewedAmiti, T., Karimi, M. A., Wüthrich, B., & Hanne, T. (2021). Using real-time traffic information for transportation planning. In R. Silhavy, P. Silhavy, & Z. Prokopova (Eds.), Data science and intelligent systems. Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2 (pp. 504–518). https://doi.org/10.1007/978-3-030-90321-3_41
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Gupta, V., Hanne, T., & Telesko, R. (2021). Requirements engineering in agile software startups - insights from multiple case studies. In R. Silhavy (Ed.), Software engineering and algorithms (Vol. 3, pp. 564–577). Springer. https://doi.org/10.1007/978-3-030-77442-4_48
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Peer reviewedPustulka, E., Hanne, T., Gachnang, P., & Biafora, P. (2021). FLIE: form labeling for information extraction. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Proceedings of the Future Technologies Conference (FTC) 2020 (Vol. 2, pp. 550–567). https://doi.org/10.1007/978-3-030-63089-8_35
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Peer reviewedWeber, L., Dornberger, R., & Hanne, T. (2021). Improved path planning with memory efficient A* algorithm and optimization of narrow passages. In A. Abraham, T. Hanne, O. Castillo, N. Gandhi, T. Nogueira Rios, & T.-P. Hong (Eds.), Hybrid Intelligent Systems. 20th International Conference on Hybrid Intelligent Systems (HIS 2020), December 14-16, 2020 (pp. 73–84). https://doi.org/10.1007/978-3-030-73050-5_8
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Peer reviewedApplewhite, T., Kaufmann, R., Dornberger, R., & Hanne, T. (2021). powerGhosts & defensiveGhosts – Enhanced ghost team controller based on Ant Colony Optimization for Ms. Pac-Man. 2021 8th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2021). November 26-27, 2021 Cairo, Egypt, 139–144. https://doi.org/10.1109/ISCMI53840.2021.9654859
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Peer reviewedYazdi, A. K., Fernandes Wanke, P., Hanne, T., Abdi, F., & Sarfaraz, A. H. (2021). Supplier selection in the oil & gas industry: a comprehensive approach for multi-criteria decision analysis. Socio-Economic Planning Sciences, 79. https://doi.org/10.1016/j.seps.2021.101142
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Peer reviewedKempter, P., Schmitz, M. P., Hanne, T., & Dornberger, R. (2021). Parameter selection for ant colony optimization for solving the travelling salesman problem based on the problem size. In A. Abraham, T. Hanne, O. Castillo, N. Gandhi, T. Nogueira Rios, & T.-P. Hong (Eds.), Hybrid Intelligent Systems. 20th International Conference on Hybrid Intelligent Systems (HIS 2020), December 14-16, 2020 (pp. 627–637). https://doi.org/10.1007/978-3-030-73050-5_61
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Peer reviewedPatan, R., Kallam, S., Gandomi, A. H., Hanne, T., & Ramachandran, M. (2021). Gaussian relevance vector MapReduce based annealed Glowworm optimization for big medical data scheduling. Journal of the Operational Research Society, 73(10), 2204–2215. https://doi.org/10.1080/01605682.2021.1960908
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Peer reviewedFix, S., Probst, T., Ruggli, O., Hanne, T., & Christen, P. (2021). Open-ended automatic programming through combinatorial evolution. In A. Abraham, N. Gandhi, T. Hanne, T.-P. Hong, T. N. Rios, & W. Ding (Eds.), Intelligent Systems Design and Applications (ISDA 2021) (pp. 1–12). Springer. https://doi.org/10.48550/arXiv.2102.10475
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Peer reviewedSaxena, U., Moulik, S., Nayak, S. R., Hanne, T., & Roy, D. S. (2021). Ensemble-based machine learning for predicting sudden human fall using health data. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/8608630
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Peer reviewedYazdi, A. K., Hanne, T., & Osorio Gómez, J. C. (2021). A hybrid model for ranking critical successful factors of lean six sigma in the oil and gas industry. The TQM Journal, 33(8), 1825–1844. https://doi.org/10.1108/TQM-02-2020-0030
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Peer reviewedWild, S., Parlar, S., Hanne, T., & Dornberger, R. (2021). Naïve Bayes and named entity recognition for requirements mining in job postings. 2021 3rd International Conference on Natural Language Processing. Proceedings, 155–161. https://doi.org/10.1109/ICNLP52887.2021.00032
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Oller, H., Dornberger, R., & Hanne, T. (2021). Improved long-short term memory U-Net for image segmentation. In S. M. Thampi, S. Krishnan, R. M. Hegde, D. Ciuonzo, T. Hanne, & J. Kannan R. (Eds.), Advances in signal processing and intelligent recognition systems. 6th International Symposium, SIRS 2020, Chennai, India, October 14-17, 2020, revised selected papers (pp. 144–154). Springer. https://doi.org/10.1007/978-981-16-0425-6_11
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Peer reviewedPustulka, E., Güler, A., & Hanne, T. (2021). A logistics serious game. GSGS′21. 6th International Conference on Gamification & Serious Game, 66–69. https://doi.org/10.26041/fhnw-6976
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Peer reviewedAjij, M., Pratihar, S., Nayak, S. R., Hanne, T., & Roy, D. S. (2021). Off-line signature verification using elementary combinations of directional codes from boundary pixels. Neural Computing and Applications, 35, 4939–4956. https://doi.org/10.1007/s00521-021-05854-6
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Peer reviewedGirardin, S., Baumann, F., Dornberger, R., & Hanne, T. (2021). Multiobjective optimization of the train staff planning problem using NSGA-II. Proceedings of 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2021), 37–43. https://doi.org/10.1145/3461598.3461604
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Peer reviewedBertini, L., Krause, K., Hanne, T., & Dornberger, R. (2021). A comparison of nearest distance optimization and ant colony optimization for order picking in a multi-aisle warehouse. ISMSI ’21: Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 1–6. https://doi.org/10.1145/3461598.3461599
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Peer reviewedGupta, V., Fernandez-Crehuet, J. M., Hanne, T., & Telesko, R. (2020). Requirements engineering in software startups: a systematic mapping study. Applied Sciences, 10(17). https://doi.org/10.3390/app10176125
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Peer reviewedGupta, V., Fernandez-Crehuet, J. M., Hanne, T., & Telesko, R. (2020). Fostering product innovations in software startups through freelancer supported requirement engineering. Results in Engineering, 8. https://doi.org/10.1016/j.rineng.2020.100175
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Peer reviewedYazdi, A. K., Fernandes Wanke, P., Hanne, T., & Bottani, E. (2020). A decision-support approach under uncertainty for evaluating reverse logistics capabilities of healthcare providers in Iran. Journal of Enterprise Information Management, 33(5), 991–1022. https://doi.org/10.1108/jeim-09-2019-0299
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Peer reviewedYazdi, A. K., Hanne, T., & Osorio Gómez, J. C. (2020). Evaluating the performance of Colombian banks by hybrid multicriteria decision making methods. Journal of Business Economics and Management, 21(6), 1707–1730. https://doi.org/10.3846/jbem.2020.11758
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Peer reviewedFaust, O., Mehli, C., Hanne, T., & Dornberger, R. (2020). A genetic algorithm for optimizing parameters for ant colony optimization solving capacitated vehicle routing problems. ISMSI′20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 52–58. https://doi.org/10.1145/3396474.3396489
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Peer reviewedGeiser, T., Hanne, T., & Dornberger, R. (2020). Best-match in a set of single-vehicle dynamic pickup and delivery problem using ant colony optimization. Proceedings of the 2020 the 3rd International Conference on Computers in Management and Business, 126–131. https://doi.org/10.1145/3383845.3383879
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Peer reviewedTallón-Ballesteros, A. J., Fong, S., Li, T., Liu, L.-s., Hanne, T., & Lin, W. (2020). Hybridized white learning in cloud-based picture archiving and communication system for predictability and interpretability. In E. A. de la Cal, J. R. Villar Flecha, H. Quintián, & E. Corchado (Eds.), Hybrid Artificial Intelligent Systems. 15th international conference, HAIS 2020, Gijón, Spain, November 11-13, 2020, proceedings (pp. 511–521). Springer. https://doi.org/10.1007/978-3-030-61705-9_42
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Peer reviewedGupta, V., Fernandez-Crehuet, J. M., Gupta, C., & Hanne, T. (2020). Freelancing models for fostering innovation and problem solving in software startups: an empirical comparative study. Sustainability, 12(23). https://doi.org/10.3390/su122310106
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Peer reviewedTrifunovic, D., Istanto, J., Hanne, T., & Dornberger, R. (2020). Hybrid genetic algorithms to solve the traveling salesman problem. In A. Abraham, T. Hanne, O. Castillo, N. Gandhi, T. Nogueira Rios, & T.-P. Hong (Eds.), Hybrid Intelligent Systems. 20th International Conference on Hybrid Intelligent Systems (HIS 2020), December 14-16, 2020 (pp. 223–233). Springer. https://doi.org/10.1007/978-3-030-73050-5_22
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Peer reviewedGupta, V., Fernandez-Crehuet, J. M., & Hanne, T. (2020). Fostering continuous value proposition innovation through freelancer involvement in software startups: Insights from multiple case studies. Sustainability, 12(21). https://doi.org/10.3390/su12218922
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Peer reviewedTroxler, D., Hanne, T., & Dornberger, R. (2020). A multi-threaded cuckoo search algorithm for the capacitated vehicle routing problem. Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2020), 105–110. https://doi.org/10.1145/3396474.3396487
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Peer reviewedEggenschwiler, S., Spahic, M., Hanne, T., & Dornberger, R. (2020). Comparison of swarm and graph algorithms for solving travelling salesman problems. 2020 7th Intl. Conference on Soft Computing Machine Intelligence (ISCMI 2020). November 14-15, 2020, Stockholm, Sweden, 1–7. https://doi.org/10.1109/ISCMI51676.2020.9311558
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Hanne, T., & Dornberger, R. (2020). Adapting the teaching of computational intelligence techniques to improve Learning Outcomes. In R. Dornberger (Ed.), New trends in business information systems and technology. Digital innovation and digital business transformation (pp. 113–129). Springer. https://doi.org/10.1007/978-3-030-48332-6_8
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Peer reviewedMurillo, F., Neuenschwander, T., Dornberger, R., & Hanne, T. (2020). Optimization of a robotic manipulation path by an evolution strategy and particle swarm optimization. ISMSI ’20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 36–41. https://doi.org/10.1145/3396474.3396488
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Peer reviewedRohr, N., Ruggli, O., Hanne, T., & Dornberger, R. (2020). Extending the whale optimization algorithm with chaotic local search. 2020 7th Intl. Conference on Soft Computing Machine Intelligence (ISCMI 2020), 28–33. https://doi.org/10.1109/ISCMI51676.2020.9311600
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Peer reviewedYazdi, A. K., Kaviani, M. A., Hanne, T., & Ramos, A. (2020). A binary differential evolution algorithm for airline revenue management: a case study. Soft Computing, 24, 14221–14234. https://doi.org/10.1007/s00500-020-04790-2
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Pustulka, E., & Hanne, T. (2020). Text mining innovation for business. In R. Dornberger (Ed.), New trends in business information systems and technology. Digital innovation and digital business Transformation (pp. 49–61). Springer. https://doi.org/10.1007/978-3-030-48332-6_4
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Peer reviewedKussmann, S., Godat, Y., Hanne, T., & Dornberger, R. (2020). A new hybrid bat algorithm optimizing the capacitated vehicle routing problem. Proceedings of the 2020 the 3rd International Conference on Computers in Management and Business, 107–111. https://doi.org/10.1145/3383845.3383880
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Peer reviewedRyter, R., Hanne, T., & Dornberger, R. (2020). Effects of noisy multiobjective test functions applied to evolutionary optimization algorithms. Journal of Advances in Information Technology, 11(3), 128–134. https://doi.org/10.12720/jait.11.3.128-134
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Peer reviewedPustulka, E., Hanne, T., Adriaensen, B., Eggenschwiler, S., Kaba, E., & Wetzel, R. (2019). An experiment with an optimization game. In K. Blashki & Y. Xiao (Eds.), IADIS International Conference Interfaces and Human Computer Interaction 2019 (part of MCCSIS 2019) (pp. 173–180). https://doi.org/10.33965/g2019_201906l022
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Peer reviewedPustulka, E., Hanne, T., Richard, W., Egemen, K., Benjamin, A., Stefan, E., & Adriaensen, B. (2019). A game teaching population based optimization using teaching-learning-based optimization. GSGS′19. 4th Gamification & Serious Game Symposium, 45–48. https://irf.fhnw.ch/handle/11654/42448
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Peer reviewedLehner, J. E., Dornberger, R., Simic, R., & Hanne, T. (2019). Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities. 2019 IEEE Congress on Evolutionary Computation (CEC 2019). Proceedings, 490–496. https://doi.org/10.1109/CEC.2019.8790367
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Peer reviewedYazdi, A. K., Hanne, T., Wang, Y. J., & Wee, H.-M. (2019). A credit rating model in a fuzzy inference system environment. Algorithms, 12(7). https://doi.org/10.3390/A12070139
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Peer reviewedPustulka, E., Hanne, T., Blumer, E., & Frieder, M. (2018). Multilingual Sentiment Analysis for a Swiss Gig. In K. C. Wong (Ed.), 6th International Symposium on Computational and Business Intelligence (ISCBI 2018). https://doi.org/10.1109/iscbi.2018.00028
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Peer reviewedRiesen, K., Hanne, T., & Schmidt, R. (2018). Sketch-based user authentication with a novel string edit distance model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(3), 460–472. https://doi.org/10.1109/TSMC.2016.2601074
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Peer reviewedPustulka, E., Telesko, R., & Hanne, T. (2018). Gig work business process improvement. In K. C. Wong (Ed.), ISCBI 2018. 6th International Symposium on Computational and Business Intelligence. Proceedings (pp. 10–15). CPS. https://doi.org/10.26041/fhnw-1764
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Peer reviewedMersiovsky, T., Thekkottil, A., Hanne, T., & Dornberger, R. (2018). Optimal learning rate and neighborhood radius of Kohonen’s self-organizing map for solving the travelling salesman problem. Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 54–59. https://doi.org/10.1145/3206185.3206194
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Peer reviewedYazdi, A. K., Hanne, T., Osorio Gómez, J. C., & García Alcaraz, J. L. (2018). Finding the best third-party logistics in the automobile industry. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/5251261
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Peer reviewedZhong, J., Dornberger, R., & Hanne, T. (2018). Comparison of the behavior of swarm robots with their computer simulations applying target-searching algorithms. International Journal of Mechanical Engineering and Robotics Research, 7(5), 507–514. https://doi.org/10.18178/IJMERR.7.5.507-514
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Peer reviewedZhong, J., Umamaheshwarappa, R. R., Dornberger, R., & Hanne, T. (2018). Comparison of a real kilobot robot implementation with its computer simulation focussing on target-searching algorithms. 2018 International Conference on Intelligent Autonomous Systems (ICoIAS’2018), 160–164. https://doi.org/10.1109/ICOIAS.2018.8494196
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Peer reviewedSong, Q., Fong, S., Deb, S., & Hanne, T. (2018). Gaussian guided self-adaptive wolf search algorithm. Entropy, 20(1). https://doi.org/10.3390/e20010037
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Hanne, T. (2017, October). Die Biologie als Wegweiser. Unternehmer Zeitung, 9/10, 30–31. https://doi.org/10.26041/fhnw-1178
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Peer reviewedMeier, D., Tullumi, I., Stauffer, Y., Dornberger, R., & Hanne, T. (2017, August). A Novel Backup Path Planning Approach with ACO. 5th International Symposium on Computational and Business Intelligence (ISCBI). http://hdl.handle.net/11654/25241
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Peer reviewedHanne, T., & Dornberger, R. (2017). Computational intelligence in logistics and supply chain management. Springer. http://hdl.handle.net/11654/24635
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Peer reviewedDornberger, R., & Hanne, T. (2017). Problem-Based Learning in Teaching the Module “Optimization for Business Improvement”. The 8th International Conference on Education, Training and Informatics: ICETI 2017. 8th International Conference on Education, Training and Informatics (ICETI 2017). http://hdl.handle.net/11654/24448
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Hanne, T., Deb, S., & Fong, S. (2016). Special issue on Recent Advances in Machine Intelligence. Soft Computing, 20(9). http://hdl.handle.net/11654/24611
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Hanne, T., Deb, S., & Fong, S. (2016). Recent advances in machine intelligence. Soft Computing, 20(9), 3347–3348. https://doi.org/10.1007/s00500-016-2276-x
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Hanne, T., Fong, S., & Deb, S. (2016). Eidetic Wolf Search Algorithm with a Global Memory Structure. European Journal of Operational Research, 254(1), 19–28. http://hdl.handle.net/11654/24610
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Peer reviewedMenon, D., Zwimpfer, C., Hanne, T., & Dornberger, R. (2015, December 9). Facility Layout Planning Using Fuzzy Closeness Computation and a Genetic Algorithm. 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015). http://hdl.handle.net/11654/5067
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Peer reviewedYang, X.-S., Deb, S., Hanne, T., & He, X. (2015). Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications, 31(7), 1987–1994. https://doi.org/10.1007/s00521-015-1925-9
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Peer reviewedStauffer, M., Ryter, R., Hanne, T., & Dornberger, R. (2015, May). Analysis of Chaotic Maps Applied to Kohonen Self-organizing Maps for the Traveling Salesman Problem. Proceedings of the Annual IEEE Congress on Evolutionary Computation (IEEE CEC 2015). http://hdl.handle.net/11654/6340
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Peer reviewedNienhold, D., Schwab, R., Dornberger, R., & Hanne, T. (2015). Effects of weight initialization in a feedforward neural network for classification using a modified genetic algorithm. 2015 3rd International Symposium on Computational and Business Intelligence. Proceedings. 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015). https://doi.org/10.1109/ISCBI.2015.9
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Hanne, T. (2015). Advances in Intelligent Informatics, Advances in Intelligent Systems and Computing. In E.-S. M. El-Alfy, S. M. Thampi, H. Takagi, S. Piramuthu, & T. Hanne (Eds.), Advances in Intelligent Informatics (Vol. 320). Springer. http://hdl.handle.net/11654/9023
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Peer reviewedAffolter, K., Hanne, T., Schweizer, D., & Dornberger, R. (2015). Invasive weed optimization for solving index tracking problems. Soft Computing, 20, 3393–3401. https://doi.org/10.1007/s00500-015-1799-x
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Peer reviewedHanne, T., Deb, S., & Fong, S. (2015). Metaheuristics in Logistics. In L. Moutinho & K.-H. Huarng (Eds.), Quantitative Modelling in Marketing and Management (2 ed.). World Scientific. https://doi.org/10.1142/9789814696357_0016
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Peer reviewedDornberger, R., Hanne, T., Gupta, V., Gupta, C., & Srivastav, M. (2015). Software Life Cycle Management Focusing on Validation in Software Applications. International Journal of Computer Aided Engineering and Technology, 7(3), 305–420. http://hdl.handle.net/11654/6338
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Peer reviewedWu, Y., Fong, S., Deb, S., He, X., & Hanne, T. (2015). Shopping Furniture Online via Intelligent Agent as an Artificial Neural Adviser. In L. Moutinho & K.-H. Huarng (Eds.), Quantitative Modelling in Marketing and Management. World Scientific. http://hdl.handle.net/11654/6331
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Peer reviewedStauffer, M., Ryter, R., Davendra, D., Dornberger, R., & Hanne, T. (2014, December 9). Genetic algorithm with embedded Ikeda map applied on an order picking problem in a multi-aisle warehouse. IEEE Symposium Series on Computational Intelligence (SSCI 2014). http://hdl.handle.net/11654/9674
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Peer reviewedKückens, M., Dornberger, R., & Hanne, T. (2013, April 19). An Approach of Solving Itinerary Construction Problems Using Real Life Data. 2013 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2013. http://hdl.handle.net/11654/8878
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Dornberger, R., Ernst, R., Frey, L., & Hanne, T. (2012). Solving Optimization Problems by Metaheuristics Using the OpenOpal-Framework - Integration of Travelling Salesman Problem and Selected Solvers. Arbeitsberichte der Hochschule für Wirtschaft FHNW. http://hdl.handle.net/11654/9418
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Akabuilo, E., Dornberger, R., & Hanne, T. (2011). How Advanced are Advanced Planning Systems? An Empirical Investigation on the Usage of Advanced Methods in APS. Arbeitsberichte der Hochschule für Wirtschaft FHNW. http://hdl.handle.net/11654/8664
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Peer reviewedDornberger, R., & Hanne, T. (2011, March 27). E-Learning and Learning-by-Doing Teaching in Information Systems - Changing the style of teaching in the Information Systems Program. The 2nd International Conference on Education, Training and Informatics: ICETI 2011. http://hdl.handle.net/11654/8669
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Dornberger, R., Hanne, T., & Frey, L. (2010). The Way to an Open-Source Software for Automated Optimization and Learning - Open Opal. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, 1–8. http://hdl.handle.net/11654/9191
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Frey, L., Hanne, T., & Dornberger, R. (2009, June 28). Optimizing staff rosters for emergency shifts for doctors. CEC 2009 - IEEE Congress on Evolutionary Computation (Proceedings). http://hdl.handle.net/11654/9586
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Hanne, T., Dornberger, R., & Frey, L. (2009, June 18). Multiobjective and Preference-Based Decision Support for Rail Crew Rostering. CEC 2009 - IEEE Congress on Evolutionary Computation (Proceedings). http://hdl.handle.net/11654/8807
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Hanne, T., Melo, T., & Nickel, S. (2009). Bringing robustness to patient flow management through optimized patient transports in hospitals. Interfaces, 39(3), 241–255. http://hdl.handle.net/11654/8981
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Hanne, T. (2009). On utilizing infeasibility in multiobjective evolutionary algorithms. In V. Barichard, M. Ehrgott, X. Gandibleux, & V. T’Kindt (eds.), Multiobjective programming and goal programming. Theoretical results and practical applications (pp. 113–122). Springer. https://doi.org/10.1007/978-3-540-85646-7
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Dornberger, R., Hanne, T., & Frey, L. (2008, June 1). Single and Multiobjective Optimization of the Train Staff Planning Problem Using Genetic Algorithms. Proceedings of the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008). 2008 IEEE World Congress on Computational Intelligence (WCCI 2008). http://hdl.handle.net/11654/9657
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Hanne, T. (2008). Fallstudie Swiss Post Solutions / cablecom: Archive as a Service. In R. Wölfle & P. Schubert (eds.), Wettbewerbsvorteile in der Kundenbeziehung durch Business Software (pp. 253–286). Hanser. https://doi.org/10.26041/fhnw-3172
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Hanne, T., & Gal, T. (2008). Some aspects of polytope degeneracy in multicriteria decision making and an outlook. In A. Bortfeldt, J. Homberger, H. Kopfer, G. Pankratz, & R. Stangmeier (Eds.), Intelligent Decision Support - Current Challenges and Approaches (pp. 303–314). Gabler. https://doi.org/10.1007/978-3-8349-9777-7_18
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No peer reviewed content available
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SEEK!
1.7.2012–30.9.2014, Hanne. Thomas
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No peer reviewed content available
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Peer reviewedPustulka, E., Hanne, T., & de Espona, L. (2021). FLIE with rules. SwissText 2021. https://irf.fhnw.ch/handle/11654/43051
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Peer reviewedPustulka, E., & Hanne, T. (2019). Sentiment analysis for a swiss gig platform company. 4th Swiss Text Analytics Conference (SwissText 2019). https://irf.fhnw.ch/handle/11654/42452
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Peer reviewedSidler, M. M., von Rohr, C., Hanne, T., & Dornberger, R. (2017, March 25). Emotion Influenced Robotic Path Planning. ISMSI 2017, International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. http://hdl.handle.net/11654/23812
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Peer reviewedButty, X., Stuber, T., Hanne, T., & Dornberger, R. (2016, September 5). A Heuristic Comparison Framework for Solving the Two-Echelon Vehicle Routing Problem. 4th International Symposium on Computational and Business Intelligence (ISCBI 2016). http://hdl.handle.net/11654/23827
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Peer reviewedSchädler, P., Berdugo Valenzuela, J. D., Hanne, T., & Dornberger, R. (2016, September 5). A Distance-Based Pareto Evolutionary Algorithm Based on SPEA for Combinatorial Problems. 4th International Symposium on Computational and Business Intelligence (ISCBI 2016). http://hdl.handle.net/11654/23817
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Peer reviewedMeier, P., Künzli, M., Hanne, T., & Dornberger, R. (2016, September 5). A Memory Search Algorithm for Path Finding Problems Compared with a Genetic Algorithm. 4th International Symposium on Computational and Business Intelligence (ISCBI 2016). http://hdl.handle.net/11654/23815
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Peer reviewedLammel, B., Gryzlak, K., Hanne, T., & Dornberger, R. (2016, September 5). An Ant Colony System Solving the Traveling Salesman Region Problem. 4th International Symposium on Computational and Business Intelligence (ISCBI 2016). http://hdl.handle.net/11654/23816
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Hunkeler, I., Schär, F., Hanne, T., & Dornberger, R. (2016, July 24). fairGhosts – Ant Colony Controlled Ghosts for Ms. Pac-Man. IEEE World Congress on Computational Intelligence (IEEE WCCI 2016). http://hdl.handle.net/11654/24207
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Stauffer, M., Hanne, T., & Dornberger, R. (2016, July 24). Uniform and Non-Uniform Pseudorandom Number Generators in a Genetic Algorithm Applied to an Order Picking Problem. IEEE World Congress on Computational Intelligence (IEEE WCCI 2016). http://hdl.handle.net/11654/23831
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Peer reviewedFong, S., Zhuang, Y., Deb, S., & Hanne, T. (2014, December 8). Solving the Permutation Flow Shop Problem with Firefly Algorithm. 2nd International Symposium on Computational and Business Intelligence (ISCBI-II 2014). http://hdl.handle.net/11654/8829
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Peer reviewedAffolter, K., Dornberger, R., Hanne, T., & Schweizer, D. (2014, September 27). Index Tracking with Invasive Weed Optimization. International Conference on Soft Computing & Machine Intelligence (ISCMI2014). http://hdl.handle.net/11654/9258
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Hanne, T. (2014, September 27). Applying metaheuristics in real-life problems. 2014 International Conference on Soft Computing and Machine Intelligence (ISCMI 2014). http://hdl.handle.net/11654/10052
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Peer reviewedDornberger, R., Hanne, T., Ryter, R., & Stauffer, M. (2014, July 11). Optimization of the Picking Sequence of an Automated Storage and Retrieval System (AS/RS). IEEE World Congress on Computational Intelligence (IEEE WCCI 2014), July 6-11th, 2014. http://hdl.handle.net/11654/9350
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Peer reviewedDornberger, R., & Hanne, T. (2014, March 7). Problem-Based Learning in Teaching Information Systems - Experiences in Teaching Computational Intelligence. The 5th International Conference on Education, Training and Informatics, ICETI. http://hdl.handle.net/11654/8867
Contact
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Prof. Dr. Thomas Hanne
- Lecturer, Institute for Information Systems
- Telephone
- +41 62 957 22 92 (direct)
- dGhvbWFzLmhhbm5lQGZobncuY2g=
- FHNW University of Applied Sciences and Arts Northwestern Switzerland
School of Business
Riggenbachstrasse 16
CH – 4600 Olten