Prof. Dr. Thomas Hanne
Prof. Dr. Thomas Hanne
Tätigkeiten an der FHNW
- Leiter Kompetenzschwerpunkt Systems Engineering
- Dozent für Wirtschaftsinformatik
Thomas Hanne ist Dozent für Wirtschaftsinformatik und Leiter des Kompetenzschwerpunkts Systems Engineering.
Lehrtätigkeiten von Thomas Hanne im 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)
- Betreuung von Studierendenprojekten und Master-Arbeiten
Lehrtätigkeiten von Thomas Hanne im Bachelorstudiengang Wirtschaftsinformatik
- Logistik und Supply Chain Management
- Operations Research
- Betreuung von Studierendenprojekten und Bachelor-Arbeiten
- kontinuierliche und kombinatorische Optimierung
- evolutionäre Algorithmen und Metaheuristiken
- Mehrzieloptimierung und Entscheidungsunterstützung
- ereignisdiskrete Simulation
- Anwendungen in der Logistik und im Supply Chain Management
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Keine peer-reviewed Inhalte verfügbar
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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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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-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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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-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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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-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-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-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-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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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-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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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-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-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-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-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-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-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-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-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-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-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-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-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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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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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-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-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-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-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-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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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-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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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-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-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-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-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-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., 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-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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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-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., 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-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-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-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-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-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-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-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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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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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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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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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-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-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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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-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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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-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., & 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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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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Keine peer-reviewed Inhalte verfügbar
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SEEK!
1.7.2012–30.9.2014, Hanne. Thomas
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Keine peer-reviewed Inhalte verfügbar
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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
Kontakt
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Prof. Dr. Thomas Hanne
- Dozent, Institut für Wirtschaftsinformatik
- Telefonnummer
- +41 62 957 22 92 (Direkt)
- dGhvbWFzLmhhbm5lQGZobncuY2g=
- Fachhochschule Nordwestschweiz FHNW
Hochschule für Wirtschaft
Riggenbachstrasse 16
4600 Olten