Hanne, Thomas

Profilbild
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Hanne
Vorname
Thomas
Name
Hanne, Thomas

Filter

2008 - 200972010 - 2019572020 - 202348
Filter zurücksetzen

Einstellungen

Suchergebnisse

Gerade angezeigt 1 - 10 von 113
  • Vorschaubild
    Publikation
    Determination of weights for multiobjective combinatorial optimization in incident management with an evolutionary algorithm
    (IEEE, 2023) Gachnang, Phillip; Ehrenthal, Joachim; Telesko, Rainer; Hanne, Thomas [in: IEEE Access]
    Incident 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=1
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Automatic programming as an open-ended evolutionary system
    (Machine Intelligence Research Labs, 2022) Fix, Sebastian; Probst, Thomas; Ruggli, Oliver; Hanne, Thomas; Christen, Patrik [in: International Journal of Computer Information Systems and Industrial Management Applications]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Analyzing the investment behavior in the Iranian stock exchange during the COVID-19 pandemic using hybrid DEA and data mining techniques
    (Hindawi, 2022) Sarfaraz, Amir Homayoun; Yazdi, Amir Karbassi; Hanne, Thomas; Gizem, Özaydin; Khalili-Damghani, Kaveh; Husseinagha, Saiedeh Molla [in: Mathematical Problems in Engineering]
    The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Quantum computing in supply chain management state of the art and research directions
    (Diponegoro University, 2022) Gachnang, Phillip; Ehrenthal, Joachim; Hanne, Thomas; Dornberger, Rolf [in: Asian Journal of Logistics Management]
    Quantum 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 Zeitschrift
  • Vorschaubild
    Publikation
    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, Zahra [in: Cogent Engineering]
    This 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 Zeitschrift
  • Vorschaubild
    Publikation
    Building a technology recommender system using web crawling and natural language processing technology
    (MDPI, 2022) Campos Macias-Hammel, Nathalie; Düggelin, Wilhelm; Ruf, Yesim; Hanne, Thomas [in: Algorithms]
    Finding, 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 Zeitschrift
  • Vorschaubild
    Publikation
    Optimization of artificial landscapes with a hybridized firefly algorithm
    (Engineering and Technology Publishing, 2022) Saner, Kevin; Smith, Kyle; Hanne, Thomas; Dornberger, Rolf [in: Journal of Advances in Information Technology]
    This paper shows how the metaheuristic Firefly Algorithm (FA) can be enhanced by hybridization with a genetic algorithm to achieve better results for optimization problems. The authors examine which configuration of the hybridized FA performs best during a number of computational tests. The performance of the hybrid FA is compared with that of the regular FA in solving test functions for single-objective optimization problems in two and n-dimensional spaces. The key findings are that more complex optimization problems benefit from the hybrid FA because it outperforms the basic FA. In addition, some useful parameters settings for the suggested algorithm are determined.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    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, Thomas [in: International Journal of Mechanical Engineering and Robotics Research]
    Q 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 Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Artificial 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
  • Vorschaubild
    Publikation
    Ensemble-based machine learning for predicting sudden human fall using health data
    (Hindawi, 2021) Saxena, Utkarsh; Moulik, Soumen; Nayak, Soumya Ranjan; Hanne, Thomas; Roy, Diptendu Sinha [in: Mathematical Problems in Engineering]
    We attempt to predict the accidental fall of human beings due to sudden abnormal changes in their health parameters such as blood pressure, heart rate, and sugar level. In medical terminology, this problem is known as Syncope. The primary motivation is to prevent such falls by predicting abnormal changes in these health parameters that might trigger a sudden fall. We apply various machine learning algorithms such as logistic regression, a decision tree classifier, a random forest classifier, K-Nearest Neighbours (KNN), a support vector machine, and a naive Bayes classifier on a relevant dataset and verify our results with the cross-validation method. We observe that the KNN algorithm provides the best accuracy in predicting such a fall. However, the accuracy results of some other algorithms are also very close. Thus, we move one step further and propose an ensemble model, Majority Voting, which aggregates the prediction results of multiple machine learning algorithms and finally indicates the probability of a fall that corresponds to a particular human being. The proposed ensemble algorithm yields 87.42% accuracy, which is greater than the accuracy provided by the KNN algorithm.
    01A - Beitrag in wissenschaftlicher Zeitschrift