Hanne, Thomas
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Publikation Determination of weights for multiobjective combinatorial optimization in incident management with an evolutionary algorithm(IEEE, 2023) Gachnang, Phillip; Ehrenthal, Joachim; Telesko, Rainer; Hanne, ThomasIncident management in railway operations includes dealing with complex and multiobjective planning problems with numerous constraints, usually with incomplete information and under time pressure. An incident should be resolved quickly with minor deviations from the original plans and at acceptable costs. The problem formulation usually includes multiple objectives relevant to a railway company and the employees involved in controlling operations. Still, there is little established knowledge and agreement regarding the relative importance of objectives such as expressed by weights. Due to the difficulties in assessing weights in a multiobjective context directly involving decision makers, we elaborate on the autoconfiguration of weighted fitness functions based on nine objectives used in a related Integer Linear Programming (ILP) problem. Our approach proposes an evolutionary algorithm and tests it on real-world railway incident management data. The proposed method outperforms the baseline, where weights are equally distributed. Thus, the algorithm shows the capability to learn weights in future applications based on the priorities of employees solving railway incidents which is not yet possible due to an insufficient availability of real-life data from incident management. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10339298&tag=101A - Beitrag in wissenschaftlicher ZeitschriftPublikation Echtzeit Ressourcendisposition von Personal und Rollmaterial in der Eisenbahnbranche(Innosuisse, 2023) Ehrenthal, Joachim; Hanne, Thomas; Telesko, Rainer; Gachnang, PhillipZu wenig Personal und Rollmaterial, kurzfristig angesagte Arbeiten an der Infrastruktur mit den entsprechenden betrieblichen Behinderungen und Einschränkungen sowie kurzfristig auftretende Störungen prägen zurzeit die Berichterstattung über die Entwicklungen im öffentlichen Verkehr der Schweiz. Es ist absehbar, dass sich diese unbefriedigende Situation über eine längere Zeitspanne kaum massgeblich verbessern wird. Umso wichtiger ist es, vorhandene Ressourcen optimal einzusetzen und den zukünftigen Bedarf an Mitarbeitenden und Rollmaterial in den Griff zu kriegen. Die Fachhochschulen der Ostschweiz (OST) und der Nordwestschweiz FHNW entwickelten mit der Südostbahn (SOB), den luxemburgischen Eisenbahnen (CFL) und der Eisenbahn-Softwareherstellerin Qnamic eine zukunftsweisende Software zur Unterstützung der Eisenbahn-Disposition, um in Echtzeit über situationsspezifische Massnahmenpakete zur Störungsbehebung zu verfügen.05 - Forschungs- oder ArbeitsberichtPublikation Optimized Computational Diabetes Prediction with Feature Selection Algorithms(2023) Li, Xi; Curiger, Michèle; Dornberger, Rolf; Hanne, Thomas04B - Beitrag KonferenzschriftPublikation Computational Intelligence in Logistik und Supply Chain Management(Springer Gabler, 2023) Hanne, Thomas; Dornberger, RolfPräsentiert den aktuellen Stand der Technik beim Einsatz von Computational Intelligence in der Lieferkette. Behandelt Probleme in den Bereichen Bestands- und Produktionsplanung, Scheduling, Transportplanung. Überprüft die verfügbare Software und Informationssysteme für jeden der behandelten Problembereiche.02 - MonographiePublikation Building a technology recommender system using web crawling and natural language processing technology(MDPI, 2022) Campos Macias-Hammel, Nathalie; Düggelin, Wilhelm; Ruf, Yesim; Hanne, ThomasFinding, retrieving, and processing information on technology from the Internet can be a tedious task. This article investigates if technological concepts such as web crawling and natural language processing are suitable means for knowledge discovery from unstructured information and the development of a technology recommender system by developing a prototype of such a system. It also analyzes how well the resulting prototype performs in regard to effectivity and efficiency. The research strategy based on design science research consists of four stages: (1) Awareness generation; (2) suggestion of a solution considering the information retrieval process; (3) development of an artefact in the form of a Python computer program; and (4) evaluation of the prototype within the scope of a comparative experiment. The evaluation yields that the prototype is highly efficient in retrieving basic and rather random extractive text summaries from websites that include the desired search terms. However, the effectivity, measured by the quality of results is unsatisfactory due to the aforementioned random arrangement of extracted sentences within the resulting summaries. It is found that natural language processing and web crawling are indeed suitable technologies for such a program whilst the use of additional technology/concepts would add significant value for a potential user. Several areas for incremental improvement of the prototype are identified.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Quantum computing in supply chain management state of the art and research directions(Diponegoro University, 2022) Gachnang, Phillip; Ehrenthal, Joachim; Hanne, Thomas; Dornberger, RolfQuantum computing is the most promising computational advance of the coming decade for solving the most challenging problems in supply chain management and logistics. This paper reviews the state-of-the-art of quantum computing and provides directions for future research. First, general concepts relevant to quantum computers and quantum computing are introduced. Second, the dominating quantum technologies are presented. Third, the quantum industry is analyzed, and recent applications in different fields of supply chain management and logistics are illustrated. Fourth, directions for future research are given. We hope this review to educate and inspire the use of quantum computing in the fields of optimization, artificial intelligence, and machine learning for supply chain and logistics.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Artificial intelligence and machine learning for maturity evaluation and model validation(2022) Hanne, Thomas; Gachnang, Phillip; Gatziu Grivas, Stella; Kirecci, Ilyas; Schmitter, PaulIn 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 KonferenzschriftPublikation Identifying and prioritizing export-related CSFs of steel products using hybrid multi-criteria methods(Taylor & Francis, 2022) Monajemzadeh, Nazli; Karbassi Yazdi, Amir; Hanne, Thomas; Shirbadadi, Shayan; Khosravi, ZahraThis research aims to identify the factors that affect the export of steel products and then prioritize them using the Weighted Aggregated Sum Product Assessment (WASPAS) method. This industry has a crucial role in various countries and the company involved in the case study is one of the three largest steel exporters in Iran. In our study, 56 effective factors have been extracted and classified after a review of the literature in the area of export and marketing, especially the export and marketing of steel products. For identifying the factors affecting the steel products export, the Delphi method was used. This method identified 26 effective factors. In the third part of the study, these effective factors were prioritized using Shannon’s entropy and the WASPAS method in combination. As a result, we have recognized three factors that are the most important to affect the export of steel products: problems and questions of export, transport aspects, and skills/knowledge.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Robotic path planning by Q learning and a performance comparison with classical path finding algorithms(Engineering and Technology, 2022) Chintala, Phalgun Chowdhary; Dornberger, Rolf; Hanne, ThomasQ Learning is a form of reinforcement learning for path finding problems that does not require a model of the environment. It allows the agent to explore the given environment and the learning is achieved by maximizing the rewards for the set of actions it takes. In the recent times, Q Learning approaches have proven to be successful in various applications ranging from navigation systems to video games. This paper proposes a Q learning based method that supports path planning for robots. The paper also discusses the choice of parameter values and suggests optimized parameters when using such a method. The performance of the most popular path finding algorithms such as A* and Dijkstra algorithm have been compared to the Q learning approach and were able to outperform Q learning with respect to computation time and resulting path length.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Automatic programming as an open-ended evolutionary system(Machine Intelligence Research Labs, 2022) Fix, Sebastian; Probst, Thomas; Ruggli, Oliver; Hanne, Thomas; Christen, Patrik01A - Beitrag in wissenschaftlicher Zeitschrift