@inproceedings{LorenzAltherrPelz2020, author = {Lorenz, Imke-Sophie and Altherr, Lena and Pelz, Peter F.}, title = {Assessing and Optimizing the Resilience of Water Distribution Systems Using Graph-Theoretical Metrics}, series = {Operations Research Proceedings 2019}, booktitle = {Operations Research Proceedings 2019}, editor = {Neufeld, Janis S. and Buscher, Udo and Lasch, Rainer and M{\"o}st, Dominik and Sch{\"o}nberger, J{\"o}rn}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-48439-2}, doi = {10.1007/978-3-030-48439-2_63}, pages = {521 -- 527}, year = {2020}, abstract = {Water distribution systems are an essential supply infrastructure for cities. Given that climatic and demographic influences will pose further challenges for these infrastructures in the future, the resilience of water supply systems, i.e. their ability to withstand and recover from disruptions, has recently become a subject of research. To assess the resilience of a WDS, different graph-theoretical approaches exist. Next to general metrics characterizing the network topology, also hydraulic and technical restrictions have to be taken into account. In this work, the resilience of an exemplary water distribution network of a major German city is assessed, and a Mixed-Integer Program is presented which allows to assess the impact of capacity adaptations on its resilience.}, language = {en} } @inproceedings{LeiseSimonAltherr2020, author = {Leise, Philipp and Simon, Nicolai and Altherr, Lena}, title = {Comparison of Piecewise Linearization Techniques to Model Electric Motor Efficiency Maps: A Computational Study}, series = {Operations Research Proceedings 2019}, booktitle = {Operations Research Proceedings 2019}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-48439-2}, doi = {10.1007/978-3-030-48439-2_55}, pages = {457 -- 463}, year = {2020}, abstract = {To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.}, language = {en} } @book{Altherr2016, author = {Altherr, Lena}, title = {Algorithmic System Design under Consideration of Dynamic Processes}, publisher = {Shaker}, address = {Aachen}, isbn = {978-3-8440-4848-3}, pages = {94}, year = {2016}, abstract = {Nach Stand von Wissenschaft und Technik werden Komponenten hinsichtlich ihrer Eigenschaften, wie Lebensdauer oder Energieeffizienz, optimiert. Allerdings k{\"o}nnen selbst hervorragende Komponenten zu ineffizienten oder instabilen Systemen f{\"u}hren, wenn ihr Zusammenspiel nur unzureichend ber{\"u}cksichtigt wird. Eine Systembetrachtung schafft ein gr{\"o}ßeres Optimierungspotential - dem erh{\"o}hten Potential steht jedoch auch ein erh{\"o}hter Komplexit{\"a}tsgrad gegen{\"u}ber. Die vorliegende Arbeit ist im Rahmen des Sonderforschungsbereichs 805 entstanden, dessen Ziel die Beherrschung von Unsicherheit in Systemen des Maschinenbaus ist. Die Arbeit zeigt anhand eines realen Systems aus dem Bereich der Hydraulik, wie Unsicherheit in der Entwicklungsphase beherrscht werden kann. Hierbei ist neu, dass die durch den sp{\"a}teren Betrieb zu erwartende Systemdegradation eines jeden m{\"o}glichen Systemvorschlags antizipiert werden kann. Dadurch k{\"o}nnen Betriebs- und Wartungskosten vorausgesagt und minimiert werden und durch eine optimale Betriebs- und Wartungsstrategie die Verf{\"u}gbarkeit des Systems garantiert werden. Wesentliche Fragen bei der optimalen Auslegung des betrachteten hydrostatischen Getriebes sind dessen physikalische Modellierung, die Darstellung des Optimierungsproblems als gemischt-ganzzahliges lineares Programm, und dessen algorithmische Behandlung zur L{\"o}sungsfindung. Hierzu werden Heuristiken zum schnelleren Auffinden sinnvoller Systemtopologien vorgestellt und mittels mathematischer Dekomposition eine Bewertung des dynamischen Verschleiß- und Wartungsverlaufs m{\"o}glicher Systemvorschl{\"a}ge vorgenommen. Die Arbeit stellt die Optimierung technischer Systeme an der Schnittstelle von Mathematik, Informatik und Ingenieurwesen sowohl gr{\"u}ndlich als auch anschaulich und nachvollziehbar dar.}, language = {en} } @incollection{AltherrEdererLorenzetal.2016, author = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp}, title = {Designing a feedback control system via mixed-integer programming}, series = {Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research}, booktitle = {Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research}, editor = {L{\"u}bbecke, Marco E. and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Preis, Britta and Walther, Grit}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-28695-2}, doi = {10.1007/978-3-319-28697-6_18}, pages = {121 -- 127}, year = {2016}, abstract = {Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.}, language = {en} } @incollection{PfetschAbeleAltherretal.2021, author = {Pfetsch, Marc E. and Abele, Eberhard and Altherr, Lena and B{\"o}lling, Christian and Br{\"o}tz, Nicolas and Dietrich, Ingo and Gally, Tristan and Geßner, Felix and Groche, Peter and Hoppe, Florian and Kirchner, Eckhard and Kloberdanz, Hermann and Knoll, Maximilian and Kolvenbach, Philip and Kuttich-Meinlschmidt, Anja and Leise, Philipp and Lorenz, Ulf and Matei, Alexander and Molitor, Dirk A. and Niessen, Pia and Pelz, Peter F. and Rexer, Manuel and Schmitt, Andreas and Schmitt, Johann M. and Schulte, Fiona and Ulbrich, Stefan and Weigold, Matthias}, title = {Strategies for mastering uncertainty}, series = {Mastering uncertainty in mechanical engineering}, booktitle = {Mastering uncertainty in mechanical engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78353-2}, doi = {10.1007/978-3-030-78354-9_6}, pages = {365 -- 456}, year = {2021}, abstract = {This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.}, language = {en} } @inproceedings{MuellerSchmittLeiseetal.2021, author = {M{\"u}ller, Tim M. and Schmitt, Andreas and Leise, Philipp and Meck, Tobias and Altherr, Lena and Pelz, Peter F. and Pfetsch, Marc E.}, title = {Validation of an optimized resilient water supply system}, series = {Uncertainty in Mechanical Engineering}, booktitle = {Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-77255-0}, doi = {10.1007/978-3-030-77256-7_7}, pages = {70 -- 80}, year = {2021}, abstract = {Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.}, language = {en} } @incollection{LeiseAltherrSimonetal.2019, author = {Leise, Philipp and Altherr, Lena and Simon, Nicolai and Pelz, Peter F.}, title = {Finding global-optimal gearbox designs for battery electric vehicles}, series = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019}, booktitle = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-21802-7}, doi = {10.1007/978-3-030-21803-4_91}, pages = {916 -- 925}, year = {2019}, abstract = {In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.}, 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} } @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} } @inproceedings{MuellerAltherrAholaetal.2019, author = {M{\"u}ller, Tim M. and Altherr, Lena and Ahola, Marja and Schabel, Samuel and Pelz, Peter F.}, title = {Multi-Criteria optimization of pressure screen systems in paper recycling - balancing quality, yield, energy consumption and system complexity}, series = {EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization}, booktitle = {EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization}, editor = {Rodrigues, H. C.}, publisher = {Springer International Publishing}, address = {Basel}, isbn = {978-3-319-97773-7}, doi = {10.1007/978-3-319-97773-7_105}, year = {2019}, abstract = {The paper industry is the industry with the third highest energy consumption in the European Union. Using recycled paper instead of fresh fibers for papermaking is less energy consuming and saves resources. However, adhesive contaminants in recycled paper are particularly problematic since they reduce the quality of the resulting paper-product. To remove as many contaminants and at the same time obtain as many valuable fibres as possible, fine screening systems, consisting of multiple interconnected pressure screens, are used. Choosing the best configuration is a non-trivial task: The screens can be interconnected in several ways, and suitable screen designs as well as operational parameters have to be selected. Additionally, one has to face conflicting objectives. In this paper, we present an approach for the multi-criteria optimization of pressure screen systems based on Mixed-Integer Nonlinear Programming. We specifically focus on a clear representation of the trade-off between different objectives.}, language = {en} }