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Steffen Limmer, Johannes Varga, Guenther Raidl , "Large Neighborhood Search for Electric Vehicle Fleet Scheduling", Energies Journal, 2023.
AbstractThis work considers the problem of planning how a fleet of shared electric vehicles is charged and used for serving a set of reservations. While exact approaches can be used to efficiently solve small to medium-sized instances of this problem, heuristic approaches have been demonstrated to be superior in larger instances. The present work proposes a large neighborhood search approach for solving this problem, which employs a mixed integer linear ...
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Yannick Deller, Sebastian Schmitt, Maciej Lewenstein, Steve Lenk, Marika Federer, Fred Jendrzejewski, Philipp Hauke, Valentin Kasper , "Quantum approximate optimization algorithm for qudit systems", Physical Review A, vol. 107, pp. 062410 , 2023.
AbstractA frequent starting point of quantum computation platforms are two-state quantum systems, i.e., qubits. However, in the context of integer optimization problems, relevant to scheduling optimization and operations research, it is often more resource-efficient to employ quantum systems with more than two basis states, so-called qudits. Here, we discuss the quantum approximate optimization algorithm (QAOA) for qudit systems. We illustrate how the QA...
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Thomas Schmitt, Jens Engel, Tobias Rodemann , "Regression-Based Model Error Compensation for a Hierarchical MPC Building Energy Management System", 7th IEEE Conference on Control Technology and Applications (CCTA) 2023, 2023.
AbstractOne of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes ...
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Anna Belardinelli, Chao Wang, Michael Gienger, Daniel Tanneberg, Stephan Hasler, Theodoros Stouraitis , "AR-mediated explainability for teaching and cooperation", Second TRR 318 Conference: Measuring Understanding, 2023.
AbstractThe current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elem...
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Sebastian Brulin , "Multi-Objective Bi-level Optimization Approach for Network Design Problems", MATSim User Meeting 2023, 2023.
AbstractThis paper presents a novel study in the field of Network Design Problems (NDPs) with a particular focus on multi-stakeholder situations in city planning, addressing the complexities of balancing profit, fairness, and system efficiency. A new multi-objective bi-level optimization framework is proposed, which expands upon traditional NDP methodologies by incorporating dynamic demand and supply-side information. Our method moves beyond standard NDP...
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Ahmed Sadik, Antonello Ceravola, Frank Joublin, Jibesh Patra , "Analysis of ChatGPT on Source Code ", ArXiv, 2023.
AbstractThis paper explores the use of Large Language Models (LLMs) and in particular ChatGPT in programming, source code analysis, and code generation. LLMs and ChatGPT are built using machine learning and artificial intelligence techniques, and they offer several benefits to developers and programmers. While these models can save time and provide highly accurate results, they are not yet advanced enough to replace human programmers entirely. The paper ...
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Thiago de Jesus de Araujo Rios, Stefan Menzel, Bernhard Sendhoff , "Large Language and Text-to-3D Models for Engineering Design Optimization", arXiv, 2023.
AbstractThe current advances in generative AI for learning large neural network models with the capability to produce essays, images, music and even 3D assets from text prompts create opportunities for a manifold of disciplines. In the present paper, we study the potential of deep text-to-3D models in the engineering domain, with focus on the chances and challenges when integrating and interacting with 3D assets in computational simulation-based design o...
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Johannes Varga, Emil Karlsson, Guenther Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann , "Speeding up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks", 9th International Conference on Machine Learning, Optimization, and Data Science (LOD), 2023.
AbstractLogic-based Benders decomposition is a technique to solve optimization problems to optimality. It works by splitting the problem into a master problem, which neglects some aspects of the problem, and a subproblem, which adds cuts to the master problem to account for those aspects. These cuts need to be refi ned to achieve good overall performance. In this paper we investigate the use of Machine Learning to learn a good refi nement procedure. We a...
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Angus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Kumar Singh, Tobias Rodemann, Markus Olhofer , "Hybridizing TPOT with Bayesian Optimization", GECCO 2023, pp. 502-510, 2023.
AbstractTree-based pipeline optimization tool (TPOT) is used to automatically construct and optimize machine learning pipelines for classification or regression tasks. The pipelines are represented as trees comprising multiple data transformation and machine learning operators — each using discrete hyper-parameter spaces — and optimized with genetic programming. During the evolution process, TPOT evaluates numerous pipelines which can be challenging when...
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Muhammad Salman Yousaf, Duane Detwiler, Fabian Duddeck, Stefan Menzel, Satchit Ramnath, Nate Zurbrugg, Mariusz Bujny , "Similarity-driven Topology Optimization for Statics and Crash via Energy Scaling Method", ASME Journal of Mechanical Design (JMD), 2023.
AbstractTopology Optimization (TO) is used in the initial design phase to optimize certain objective functions under given boundary conditions by finding suitable material distributions in a specified design domain. Currently available methods in industry work very efficiently to get topologically-optimized design concepts under static and dynamic load cases. However, conventional methods do not address the designer’s preferences about the final material...
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