Gachnang, Phillip

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Gachnang
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Phillip
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Phillip Gachnang

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2020 - 20233
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  • 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
    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
    Combining symbolic and sub-symbolic AI in the context of education and learning
    (2020) Telesko, Rainer; Jüngling, Stephan; Gachnang, Phillip; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)]
    Abstraction abilities are key to successfully mastering the Business Information Technology Programme (BIT) at the FHNW (Fachhochschule Nordwestschweiz). Object-Orientation (OO) is one example - which extensively requires analytical capabilities. For testing the OO-related capabilities a questionnaire (OO SET) for prospective and 1st year students was developed based on the Blackjack scenario. Our main target of the OO SET is to identify clusters of students which are likely to fail in the OO-related modules without a substantial amount of training. For the interpretation of the data the Kohonen Feature Map (KFM) is used which is nowadays very popular for data mining and exploratory data analysis. However, like all sub-symbolic approaches the KFM lacks to interpret and explain its results. Therefore, we plan to add - based on existing algorithms - a “postprocessing” component which generates propositional rules for the clusters and helps to improve quality management in the admission and teaching process. With such an approach we synergistically integrate symbolic and sub-symbolic artificial intelligence by building a bridge between machine learning and knowledge engineering.
    04B - Beitrag Konferenzschrift