TY  - CHAP
A1  - Müller, Tim M.
A1  - Altherr, Lena
A1  - Ahola, Marja
A1  - Schabel, Samuel
A1  - Pelz, Peter F.
T1  - Optimizing pressure screen systems in paper recycling: optimal system layout, component selection and operation
N2  - Around 60% of the paper worldwide is made from recovered paper. Especially adhesive contaminants, so called stickies, reduce paper quality. To remove stickies but at the same time keep as many valuable fibers as possible, multi-stage screening systems with several interconnected pressure screens are used. When planning such systems, suitable screens have to be selected and their interconnection as well as operational parameters have to be defined considering multiple conflicting objectives. In this contribution, we present a Mixed-Integer Nonlinear Program to optimize system layout, component selection and operation to find a suitable trade-off between output quality and yield.
KW  - Mixed-integer nonlinear problem
KW  - MINLP
KW  - Process engineering
KW  - Paper recycling
KW  - Multi-criteria optimization
Y1  - 2018
SN  - 978-3-030-18499-5
U6  - http://dx.doi.org/10.1007/978-3-030-18500-8_44
SP  - 355
EP  - 361
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Pfetsch, Marc E.
A1  - Abele, Eberhard
A1  - Altherr, Lena
A1  - Bölling, Christian
A1  - Brötz, Nicolas
A1  - Dietrich, Ingo
A1  - Gally, Tristan
A1  - Geßner, Felix
A1  - Groche, Peter
A1  - Hoppe, Florian
A1  - Kirchner, Eckhard
A1  - Kloberdanz, Hermann
A1  - Knoll, Maximilian
A1  - Kolvenbach, Philip
A1  - Kuttich-Meinlschmidt, Anja
A1  - Leise, Philipp
A1  - Lorenz, Ulf
A1  - Matei, Alexander
A1  - Molitor, Dirk A.
A1  - Niessen, Pia
A1  - Pelz, Peter F.
A1  - Rexer, Manuel
A1  - Schmitt, Andreas
A1  - Schmitt, Johann M.
A1  - Schulte, Fiona
A1  - Ulbrich, Stefan
A1  - Weigold, Matthias
T1  - Strategies for mastering uncertainty
T2  - Mastering uncertainty in mechanical engineering
N2  - 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.
Y1  - 2021
SN  - 978-3-030-78353-2
U6  - http://dx.doi.org/10.1007/978-3-030-78354-9_6
N1  - Part of the Springer Tracts in Mechanical Engineering book series (STME)
SP  - 365
EP  - 456
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Stenger, David
A1  - Altherr, Lena
A1  - Abel, Dirk
T1  - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead
T2  - Operations Research Proceedings 2018
N2  - 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.
KW  - Product family optimization
KW  - Mixed-integer nonlinear black-box optimization
KW  - Engineering optimization
KW  - Machine learning
Y1  - 2019
SN  - 978-3-030-18499-5  (Print)
SN  - 978-3-030-18500-8 (Online)
U6  - http://dx.doi.org/10.1007/978-3-030-18500-8_47
SP  - 379
EP  - 385
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Stenger, David
A1  - Altherr, Lena
A1  - Müller, Tankred
A1  - Pelz, Peter F.
T1  - Product family design optimization using model-based engineering techniques
T2  - Operations Research Proceedings 2017
N2  - 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.
Y1  - 2018
SN  - 978-3-319-89919-0
U6  - http://dx.doi.org/10.1007/978-3-319-89920-6_66
SP  - 495
EP  - 502
PB  - Springer
CY  - Cham
ER  -