@article{VergePoettgenAltherretal.2016, author = {Verg{\´e}, Angela and P{\"o}ttgen, Philipp and Altherr, Lena and Ederer, Thorsten and Pelz, Peter F.}, title = {Lebensdauer als Optimierungsziel: Algorithmische Struktursynthese am Beispiel eines hydrostatischen Getriebes}, series = {O+P - {\"O}lhydraulik und Pneumatik}, volume = {60}, journal = {O+P - {\"O}lhydraulik und Pneumatik}, number = {1-2}, editor = {Greuloch, Ivo and Weber, Manfred and Meier, Miles}, publisher = {Vereinigte Fachverl.}, address = {Mainz}, issn = {1614-9602}, pages = {114 -- 121}, year = {2016}, abstract = {Verf{\"u}gbarkeit und Nachhaltigkeit sind wichtige Anforderungen bei der Planung langlebiger technischer Systeme. Meist werden bei Lebensdaueroptimierungen lediglich einzelne Komponenten vordefinierter Systeme untersucht. Ob eine optimale Lebensdauer eine g{\"a}nzlich andere Systemvariante bedingt, wird nur selten hinterfragt. Technical Operations Research (TOR) erlaubt es, aus Obermengen technischer Systeme automatisiert die lebensdaueroptimale Systemstruktur auszuw{\"a}hlen. Der Artikel zeigt dies am Beispiel eines hydrostatischen Getriebes.}, language = {de} } @inproceedings{TischbeinKeanVertgewalletal.2023, author = {Tischbein, Franziska and Kean, Kilian and Vertgewall, Chris Martin and Ulbig, Andreas and Altherr, Lena}, title = {Determination of the topology of low-voltage distribution grids using cluster methods}, series = {27th International Conference on Electricity Distribution (CIRED 2023)}, booktitle = {27th International Conference on Electricity Distribution (CIRED 2023)}, publisher = {IEEE}, isbn = {978-1-83953-855-1}, doi = {10.1049/icp.2023.0478}, pages = {1 -- 5}, year = {2023}, abstract = {Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.}, language = {en} } @article{SunAltherrPeietal.2018, author = {Sun, Hui and Altherr, Lena and Pei, Ji and Pelz, Peter F. and Yuan, Shouqi}, title = {Optimal booster station design and operation under uncertain load}, series = {Applied Mechanics and Materials}, volume = {885}, journal = {Applied Mechanics and Materials}, publisher = {Trans Tech Publications}, address = {B{\"a}ch}, issn = {1662-7482}, doi = {10.4028/www.scientific.net/AMM.885.102}, pages = {102 -- 115}, year = {2018}, abstract = {Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the system's resilience can be engineered}, language = {en} } @incollection{StengerAltherrMuelleretal.2018, author = {Stenger, David and Altherr, Lena and M{\"u}ller, Tankred and Pelz, Peter F.}, title = {Product family design optimization using model-based engineering techniques}, series = {Operations Research Proceedings 2017}, booktitle = {Operations Research Proceedings 2017}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-89919-0}, doi = {10.1007/978-3-319-89920-6_66}, pages = {495 -- 502}, year = {2018}, abstract = {Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products.}, language = {en} } @incollection{StengerAltherrAbel2019, author = {Stenger, David and Altherr, Lena and Abel, Dirk}, title = {Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead}, series = {Operations Research Proceedings 2018}, booktitle = {Operations Research Proceedings 2018}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-18499-5 (Print)}, doi = {10.1007/978-3-030-18500-8_47}, pages = {379 -- 385}, year = {2019}, abstract = {In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.}, language = {en} } @inproceedings{SchaenzleAltherrEdereretal.2015, author = {Sch{\"a}nzle, Christian and Altherr, Lena and Ederer, Thorsten and Pelz, Peter}, title = {TOR - Towards the energetically optimal ventilation system}, pages = {1 Seite}, year = {2015}, language = {en} } @inproceedings{SchaenzleAltherrEdereretal.2015, author = {Sch{\"a}nzle, Christian and Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F.}, title = {As good as it can be: Ventilation system design by a combined scaling and discrete optimization method}, series = {Proceedings of FAN 2015}, booktitle = {Proceedings of FAN 2015}, pages = {1 -- 11}, year = {2015}, abstract = {The understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans.}, language = {en} } @inproceedings{RauschLeiseEdereretal.2016, author = {Rausch, Lea and Leise, Philipp and Ederer, Thorsten and Altherr, Lena and Pelz, Peter F.}, title = {A comparison of MILP and MINLP solver performance on the example of a drinking water supply system design problem}, series = {ECCOMAS Congress 2016 VII European Congress on Computational Methods in Applied Sciences and Engineering}, booktitle = {ECCOMAS Congress 2016 VII European Congress on Computational Methods in Applied Sciences and Engineering}, editor = {Papadrakakis, M. and Ppadopoulos, V. and Stefanou, G. and Plevris, V.}, isbn = {978-618-82844-0-1}, pages = {8509 -- 8527}, year = {2016}, abstract = {Finding a good system topology with more than a handful of components is a highly non-trivial task. The system needs to be able to fulfil all expected load cases, but at the same time the components should interact in an energy-efficient way. An example for a system design problem is the layout of the drinking water supply of a residential building. It may be reasonable to choose a design of spatially distributed pumps which are connected by pipes in at least two dimensions. This leads to a large variety of possible system topologies. To solve such problems in a reasonable time frame, the nonlinear technical characteristics must be modelled as simple as possible, while still achieving a sufficiently good representation of reality. The aim of this paper is to compare the speed and reliability of a selection of leading mathematical programming solvers on a set of varying model formulations. This gives us empirical evidence on what combinations of model formulations and solver packages are the means of choice with the current state of the art.}, language = {en} } @inproceedings{RauschFriesenAltherretal.2018, author = {Rausch, Lea and Friesen, John and Altherr, Lena and Pelz, Peter F.}, title = {Using mixed-integer programming for the optimal design of water supply networks for slums}, series = {Operations Research Proceedings 2017}, booktitle = {Operations Research Proceedings 2017}, editor = {Kliewer, Natalia and Ehmke, Jan Fabian and Bornd{\"o}rfer, Ralf}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-89919-0 (Print)}, doi = {10.1007/978-3-319-89920-6_68}, pages = {509 -- 516}, year = {2018}, abstract = {The UN sets the goal to ensure access to water and sanitation for all people by 2030. To address this goal, we present a multidisciplinary approach for designing water supply networks for slums in large cities by applying mathematical optimization. The problem is modeled as a mixed-integer linear problem (MILP) aiming to find a network describing the optimal supply infrastructure. To illustrate the approach, we apply it on a small slum cluster in Dhaka, Bangladesh.}, language = {en} } @article{RauschFriesenAltherretal.2018, author = {Rausch, Lea and Friesen, John and Altherr, Lena and Meck, Marvin and Pelz, Peter F.}, title = {A holistic concept to design optimal water supply infrastructures for informal settlements using remote sensing data}, series = {Remote Sensing}, volume = {10}, journal = {Remote Sensing}, number = {2}, publisher = {MDPI}, address = {Basel}, isbn = {2072-4292}, doi = {10.3390/rs10020216}, pages = {1 -- 23}, year = {2018}, abstract = {Ensuring access to water and sanitation for all is Goal No. 6 of the 17 UN Sustainability Development Goals to transform our world. As one step towards this goal, we present an approach that leverages remote sensing data to plan optimal water supply networks for informal urban settlements. The concept focuses on slums within large urban areas, which are often characterized by a lack of an appropriate water supply. We apply methods of mathematical optimization aiming to find a network describing the optimal supply infrastructure. Hereby, we choose between different decentral and central approaches combining supply by motorized vehicles with supply by pipe systems. For the purposes of illustration, we apply the approach to two small slum clusters in Dhaka and Dar es Salaam. We show our optimization results, which represent the lowest cost water supply systems possible. Additionally, we compare the optimal solutions of the two clusters (also for varying input parameters, such as population densities and slum size development over time) and describe how the result of the optimization depends on the entered remote sensing data.}, language = {en} }