@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}
}
@article{AltherrEdererPfetschetal.2018,
  author    = {Altherr, Lena and Ederer, Thorsten and Pfetsch, Marc E. and Pelz, Peter F.},
  title     = {Maschinelles Design eines optimalen Getriebes},
  series = {ATZ - Automobiltechnische Zeitschrift},
  volume    = {120},
  journal   = {ATZ - Automobiltechnische Zeitschrift},
  number    = {10},
  publisher = {Springer Nature},
  address   = {Cham},
  isbn      = {2192-8800},
  doi       = {10.1007/s35148-018-0131-3},
  pages     = {72 -- 77},
  year      = {2018},
  abstract  = {Nahezu 100.000 denkbare Strukturen kann ein Getriebe bei gleicher Funktion aufweisen - je nach Ganganzahl und gefordertem Freiheitsgrad. Mit dem traditionellen Ansatz bei der Entwicklung, einzelne vielversprechende Systemkonfigurationen manuell zu identifizieren und zu vergleichen, k{\"o}nnen leicht innovative und vor allem kostenminimale L{\"o}sungen {\"u}bersehen werden. Im Rahmen eines Forschungsprojekts hat die TU Darmstadt spezielle Optimierungsmethoden angewendet, um auch bei großen L{\"o}sungsr{\"a}umen zielsicher ein f{\"u}r die individuellen Zielstellungen optimales Layout zu finden.},
  language  = {de}
}
@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}
}