Hanne, Thomas
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Hanne, Thomas
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- PublikationArtificial intelligence and machine learning for maturity evaluation and model validation(2022) Hanne, Thomas; Gachnang, Phillip; Gatziu Grivas, Stella; Kirecci, Ilyas; Schmitter, Paul [in: ICEME 2022. The 2022 13th International Conference on E-business, Management and Economics (ICEME 2022). Beijing, China (vurtual conference), July 16-18, 2022]In this paper, we discuss the possibility of using machine learning (ML) to specify and validate maturity models, in particular maturity models related to the assessment of digital capabilities of an organization. Over the last decade, a rather large number of maturity models have been suggested for different aspects (such as type of technology or considered processes) and in relation to different industries. Usually, these models are based on a number of assumptions such as the data used for the assessment, the mathematical formulation of the model and various parameters such as weights or importance indicators. Empirical evidence for such assumptions is usually lacking. We investigate the potential of using data from assessments over time and for similar institutions for the ML of respective models. Related concepts are worked out in some details and for some types of maturity assessment models, a possible application of the concept is discussed.04B - Beitrag Konferenzschrift
- PublikationA logistics serious game(2021) Pustulka, Elzbieta; Güler, Attila; Hanne, Thomas [in: GSGS'21. 6th International Conference on Gamification & Serious Game]Switzerland is a logistics hub which needs many trained professionals. As logistics does not have a strong public image, the profession does not attract enough young people. A logistics game could help recruit more candidates at the apprenticeship and university level and help in teaching. We have prototyped a logistics game and found out that it raises interest in logistics and successfully teaches about cargo ships. The game test showed that the game is visually appealing but the competitive aspect may interfere with learning.04B - Beitrag Konferenzschrift
- PublikationOpen-ended automatic programming through combinatorial evolution(Springer, 2021) Fix, Sebastian; Probst, Thomas; Ruggli, Oliver; Hanne, Thomas; Christen, Patrik; Abraham, Ajith; Gandhi, Niketa; Hanne, Thomas; Hong, Tzung-Pei; Rios, Tatiane Nogueira; Ding, Weiping [in: Intelligent Systems Design and Applications (ISDA 2021)]04B - Beitrag Konferenzschrift
- PublikationSolving inventory routing problems with the Gurobi Branch-and-Cut Algorithm(Springer, 2021) Meier, Danny; Keller, Benjamin; Kolb, Markus; Hanne, Thomas; Dorronsoro, Bernabé; Amodeo, Lionel; Pavone, Mario; Ruiz, Patricia [in: Optimization and Learning. 4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021. Proceedings]04B - Beitrag Konferenzschrift
- PublikationUsing real-time traffic information for transportation planning(2021) Amiti, Taulant; Karimi, Mohammad Ali; Wüthrich, Benjamin; Hanne, Thomas; Silhavy, Radek; Silhavy, Petr; Prokopova, Zdenka [in: Data science and intelligent systems. Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2]04B - Beitrag Konferenzschrift
- PublikationImproved path planning with memory efficient A* algorithm and optimization of narrow passages(2021) Weber, Lukas; Dornberger, Rolf; Hanne, Thomas; Abraham, Ajith; Hanne, Thomas; Castillo, Oscar; Gandhi, Niketa; Nogueira Rios, Tatiana; Hong, Tzung-Pei [in: Hybrid Intelligent Systems. 20th International Conference on Hybrid Intelligent Systems (HIS 2020), December 14-16, 2020]04B - Beitrag Konferenzschrift
- PublikationNaïve Bayes and named entity recognition for requirements mining in job postings(2021) Wild, Simon; Parlar, Soyhan; Hanne, Thomas; Dornberger, Rolf [in: 2021 3rd International Conference on Natural Language Processing. Proceedings]This paper analyses how the required skills in a job post can be extracted. With an automated extraction of skills from unstructured text, applicants could be more accurately matched and search engines could provide better recommendations. The problem is optimized by classifying the relevant parts of the description with a multinomial naïve Bayes model. The model identifies the section of the unstructured text in which the requirements are stated. Subsequently, a named entity recognition (NER) model extracts the required skills from the classified text. This approach minimizes the false positives since the data which is analyzed is already filtered. The results show that the naïve Bayes model classifies up to 99% of the sections correctly, and the NER model extracts 65% of the skills required for a position. The accuracy of the NER model is not sufficient to be used in production. On the validation set, the performance was insufficient. A more consistent labelling guideline would be needed and more data should be annotated to increase the performance.04B - Beitrag Konferenzschrift
- PublikationBenchmarking tabu search and simulated annealing for the capacitated vehicle routing problem(2021) Arockia, Amala; Lochbrunner, Markus; Hanne, Thomas; Dornberger, Rolf [in: ICCMB 2021. 2021 4th International Conference on Computers in Management and Business. Singapore, January 30-February 1, 2021]This paper addresses the Capacitated Vehicle Routing Problem (CVRP) consisting of a single depot and several customers that are supplied with goods by capacitated vehicles from a depot. The main objective of the vehicle routing problem is to minimize the traveled distance of all vehicles. We compare the Tabu Search (TS) and Simulated Annealing (SA) algorithm with different initial solution strategies to solve the CVRP. We run the publicly available solver on a set of benchmark problems comparing above mentioned methods and initial solutions. The results show that TS appears superior for small-sized problems, while SA has an advantage for mid-sized problems. For larger problems the preferability of a methods depends on the available run time with SA appear promising for shorter runtime and TS for longer.04B - Beitrag Konferenzschrift
- PublikationA serious game for teaching genetic algorithms(2021) Moser, Lars; Saner, Kevin; Oggier, Vincent; Hanne, Thomas; Arai, Kohei [in: Proceedings of the Future Technologies Conference (FTC) 2021]04B - Beitrag Konferenzschrift
- PublikationFLIE: form labeling for information extraction(2021) Pustulka, Elzbieta; Hanne, Thomas; Gachnang, Phillip; Biafora, Pasquale; Arai, Kohei; Kapoor, Supriya; Bhatia, Rahul [in: Proceedings of the Future Technologies Conference (FTC) 2020]Information extraction (IE) from forms remains an unsolved problem, with some exceptions, like bills. Forms are complex and the templates are often unstable, due to the injection of advertising, extra conditions, or document merging. Our scenario deals with insurance forms used by brokers in Switzerland. Here, each combination of insurer, insurance type and language results in a new document layout, leading to a few hundred document types. To help brokers extract data from policies, we developed a new labeling method, called FLIE (form labeling for information extraction). FLIE first assigns a document to a cluster, grouping by language, insurer, and insurance type. It then labels the layout. To produce training data, the user annotates a sample document by hand, adding attribute names, i.e. provides a mapping. FLIE applies machine learning to propagate the mapping and extracts information. Our results are based on 24 Swiss policies in German: UVG (mandatory accident insurance), KTG (sick pay insurance), and UVGZ (optional accident insurance). Our solution has an accuracy of around 84-89%. It is currently being extended to other policy types and languages.04B - Beitrag Konferenzschrift