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  <doc>
    <id>1885</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-07-01</completedDate>
    <publishedDate>2013-07-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Unified View on Relaxations for a Nonlinear Network Flow Problem</title>
    <abstract language="eng">We consider a nonlinear nonconvex network flow problem that arises, for example, in natural gas or water transmission networks. Given is such network with active and passive components, that is, valves, compressors, pressure regulators (active) and pipelines (passive), and a desired amount of flow at certain specified entry and exit nodes of the network. Besides flow conservation constraints in the nodes the flow must fulfill nonlinear nonconvex pressure loss constraints on the arcs subject to potential values (i.e., pressure levels) in both end nodes of each arc. The problem is how to numerically compute this flow and pressures. We review an existing approach of Maugis (1977) and extend it to the case of networks with active elements (for example, compressors). We further examine different ways of relaxations for the nonlinear network flow model. We compare different approaches based on nonlinear optimization numerically on a set of test instances.</abstract>
    <identifier type="urn">urn:nbn:de:0297-zib-18857</identifier>
    <author>Armin Fügenschuh</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Jesco Humpola</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-31</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nonlinear Network Flow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-Integer Nonlinear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Relaxations</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="ForNe">ForNe</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1885/ZR-13-31.pdf</file>
  </doc>
  <doc>
    <id>1860</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-05-27</completedDate>
    <publishedDate>2013-05-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Global Approach to the Optimal Control of System Dynamics Models</title>
    <abstract language="eng">The System Dynamics (SD) methodology is a framework for modeling and simulating the dynamic behavior of socioeconomic systems. Characteristic for the description of such systems is the occurrence of feedback loops together with stocks and flows. The mathematical equations that describe the system are usually ordinary differential equations and nonlinear algebraic constraints. Therefore seemingly simple systems can show a nonintuitive, unpredictable behavior over time. Controlling a dynamical system means to specify potential interventions from outside that should keep the system on the desired track, and to define an evaluation schema to compare different controls among each other, so that a "best" control can be defined in a meaningful way. The central question is how to compute such globally optimal control for a given SD model, that allows the transition of the system into a desired state with minimum effort. We propose a mixed-integer nonlinear programming (MINLP) reformulation of the System Dynamics Optimization (SDO) problem. MINLP problems can be solved by linear programming based branch-and-bound approach. We demonstrate that standard MINLP solvers are not able to solve SDO problem. To overcome this obstacle, we introduce a special-tailored bound propagation method. We apply our new method to a predator-prey model with additional hunting activity as control, and to a mini-world model with the consumption level as control. Numerical results for these test cases are presented.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-18600</identifier>
    <author>Armin Fügenschuh</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Ingmar Vierhaus</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-28</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>System Dynamics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Global Optimal Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-Integer Nonlinear Optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bounds Strengthening</value>
    </subject>
    <collection role="msc" number="34-XX">ORDINARY DIFFERENTIAL EQUATIONS</collection>
    <collection role="msc" number="37-XX">DYNAMICAL SYSTEMS AND ERGODIC THEORY [See also 26A18, 28Dxx, 34Cxx, 34Dxx, 35Bxx, 46Lxx, 58Jxx, 70-XX]</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1860/ZR-13-28.pdf</file>
  </doc>
  <doc>
    <id>1889</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-07-02</completedDate>
    <publishedDate>2013-07-02</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identification of trade-offs for sustainable manufacturing of a Bamboo Bike by System Dynamics</title>
    <abstract language="eng">We develop a generic System Dynamic model to simulate the production, machines, employees, waste, and capital flows of a manufacturing company. In a second step, this model is specialised by defining suit-able input data to represent a bicycle manufacturing company in a developing country. We monitor a set of sustainability indicators to understand the social, environmental and economic impact of the company, and to estimate managerial decisions to be taken in order to improve on these criteria. We show that the social and environmental situation can be improved over time without sacrificing the economic success of the company's business.</abstract>
    <identifier type="urn">urn:nbn:de:0297-zib-18895</identifier>
    <enrichment key="SourceTitle">Appeared in: Proceedings of the 27th Conference on Environmental Informatics –  Informatics for Environmental Protection, Sustainable Development and Risk Management , Sept. 2-4, 2013 Univ. Hamburg, Germany. Shaker 2013. ISBN 978-3-8440-1676-5 pp. 523-531</enrichment>
    <author>René Scheumann</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Ingmar Vierhaus</author>
    <author>Ya-Ju Chang</author>
    <author>Armin Fügenschuh</author>
    <author>Matthias Finkbeiner</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-32</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>System Dynamics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Life Cycle Sustainability Assessment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainability Indicators</value>
    </subject>
    <collection role="msc" number="91-XX">GAME THEORY, ECONOMICS, SOCIAL AND BEHAVIORAL SCIENCES</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1889/ZR-13-32.pdf</file>
  </doc>
  <doc>
    <id>1777</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-02-16</completedDate>
    <publishedDate>2013-02-16</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A New Class of Valid Inequalities for Nonlinear Network Design Problems</title>
    <abstract language="eng">We consider a nonlinear nonconvex network design problem that arises in the extension of natural gas transmission networks. Given is such network with active and passive components, that is, valves, compressors, pressure regulators (active) and pipelines (passive), and a desired amount of flow at certain specified entry and exit nodes of the network. Besides flow conservation constraints in the nodes the flow must fulfill nonlinear nonconvex pressure loss constraints on the arcs subject to potential values (i.e., pressure levels) in both end nodes of each arc. Assume that there does not exist a feasible flow that fulfills all physical constraints and meets the desired entry and exit amounts. Then a natural question is where to extend the network by adding pipes in the most economic way such that this flow becomes feasible. Answering this question is computationally demanding because of the difficult problem structure. We use mixed-integer nonlinear programming techniques that rely on an outer approximation of the overall problem, and a branching on decision variables. We formulate a new class of valid inequalities (or cutting planes) which reduce the overall solution time when added to the formulation. We demonstrate the computational merits of our approach on test instances.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-17771</identifier>
    <identifier type="doi">10.1007/s00291-015-0390-2</identifier>
    <enrichment key="SourceTitle">Appeared in: OR Spectrum 2015</enrichment>
    <author>Jesco Humpola</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Armin Fügenschuh</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-06</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Network Design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-Integer Nonlinear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cutting Planes</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="ForNe">ForNe</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1777/FuegenschuhHumpola2012__dual_var_cuts.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/1777/ZR-13-06revisedversion.pdf</file>
  </doc>
  <doc>
    <id>1782</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-03-07</completedDate>
    <publishedDate>2013-03-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mathematical Optimization for Challenging Network Planning Problems in Unbundled Liberalized Gas Markets</title>
    <abstract language="eng">The recently imposed new gas market liberalization rules in Germany lead to a change of business of gas network operators. &#13;
While previously network operator and gas vendor where united, they were forced to split up into independent companies. &#13;
The network has to be open to any other gas trader at the same conditions, and free network capacities have to be identified and publicly offered in a non-discriminatory way. &#13;
We show that these new paradigms lead to new and challenging mathematical optimization problems. &#13;
In order to solve them and to provide meaningful results for practice, all aspects of the underlying problems, such as combinatorics, stochasticity, uncertainty, and nonlinearity, have to be addressed. &#13;
With such special-tailored solvers, free network capacities and topological network extensions can, for instance, be determined.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-17821</identifier>
    <identifier type="doi">10.1007/s12667-013-0099-8</identifier>
    <enrichment key="SourceTitle">Appeared in:  Energy Systems  5 (2014) 449-473</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter>Jonas Schweiger</submitter>
    <author>Björn Geißler</author>
    <author>Ralf Gollmer</author>
    <author>Christine Hayn</author>
    <author>Rene Henrion</author>
    <author>Benjamin Hiller</author>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Alexander Martin</author>
    <author>Radoslava Mirkov</author>
    <author>Antonio Morsi</author>
    <author>Werner Römisch</author>
    <author>Jessica Rövekamp</author>
    <author>Lars Schewe</author>
    <author>Martin Schmidt</author>
    <author>Rüdiger Schultz</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Claudia Stangl</author>
    <author>Marc Steinbach</author>
    <author>Bernhard Willert</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-13</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas Market Liberalization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Entry-Exit Model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas Network Access Regulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-Integer Nonlinear Nonconvex Stochastic Optimization</value>
    </subject>
    <collection role="pacs" number="89.30.-g">Energy resources (see also 84.60.-h Direct energy conversion and storage)</collection>
    <collection role="msc" number="90B10">Network models, deterministic</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C30">Nonlinear programming</collection>
    <collection role="msc" number="90C90">Applications of mathematical programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="ForNe">ForNe</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="schweiger">Schweiger, Jonas</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1782/ZR-13-13.pdf</file>
  </doc>
  <doc>
    <id>1814</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-04-23</completedDate>
    <publishedDate>2013-04-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">System Dynamic Optimization in the Sustainability Assessment of a World-Model</title>
    <abstract language="eng">The System Dynamics (SD) methodology is a framework for modeling and simulating&#13;
the dynamic behavior of socioeconomic systems. Characteristic for the&#13;
description of such systems is the occurrence of feedback loops together with&#13;
stocks and flows. The mathematical equations that describe the system are&#13;
usually nonlinear. Therefore seemingly simple systems can show a nonintuitive,&#13;
nonpredictable behavior over time. Controlling a dynamical system means to&#13;
define a desired final state in which the system should be, and to specify&#13;
potential interventions from outside that should keep the system on the right&#13;
track. The central question is how to compute such globally optimal control for&#13;
a given SD model. We propose a branch-and-bound approach that is based on a&#13;
bound propagation method, primal heuristics, and spatial branching. We apply our&#13;
new SD-control method to a small System Dynamics model, that describes the&#13;
evolution of a social-economic system over time. We examine the problem of&#13;
steering this system on a sustainable consumption path.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-18148</identifier>
    <identifier type="url">http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4026</identifier>
    <enrichment key="SourceTitle">Appeared in: Proceedings of the 11th Global Conference on Sustainable Manufacturing (GCSM2013), G. Seliger (Hrsg.), Universitätsverlag der TU Berlin, Seite 582 -- 587</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Ingmar Vierhaus</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-23</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>System Dynamics; Mixed-Integer Nonlinear Optimization</value>
    </subject>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="projects" number="CRC1026">CRC1026</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1814/ZR-13-23.pdf</file>
  </doc>
  <doc>
    <id>1816</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-04-23</completedDate>
    <publishedDate>2013-04-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Production Planning for Non-Cooperating Companies with Nonlinear Optimization</title>
    <abstract language="eng">We consider a production planning problem where two competing companies are selling their items on a common market. Moreover, the raw material used in the production is a limited non-renewable resource. The revenue per item sold depends on the total amount of items produced by both players. If they collaborate they could apply a production strategy that leads to the highest combined revenue. Usually the formation of such syndicates is prohibited by law; hence we assume that one company does not know how much the other company will produce. We formulate the problem for company A to find an optimal production plan without information on the strategy of company B as a nonlinear mathematical optimization problem. In its naive formulation the model is too large, making its solution practically impossible. After a reformulation we find a much smaller model, which we solve by spatial branch-and-cut methods and linear programming. We discuss the practical implications of our solutions.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-18163</identifier>
    <identifier type="url">http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4026</identifier>
    <enrichment key="SourceTitle">Appeared in: Proceedings of the 11th Global Conference on Sustainable Manufacturing (GCSM2013), G. Seliger (Hrsg.), Universitätsverlag der TU Berlin, Seite 588 -- 593</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Roel van Veldhuizen</author>
    <author>Ingmar Vierhaus</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-25</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-Cooperative Two-Person Games; Mixed-Integer Nonlinear Optimization</value>
    </subject>
    <collection role="msc" number="91-XX">GAME THEORY, ECONOMICS, SOCIAL AND BEHAVIORAL SCIENCES</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="projects" number="CRC1026">CRC1026</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1816/ZR-13-25.pdf</file>
  </doc>
  <doc>
    <id>1653</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2012-11-19</completedDate>
    <publishedDate>2012-11-19</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions</title>
    <abstract language="eng">In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints.&#13;
We describe a two-stage approach to solve the resulting complex and numerically difficult mixed-integer non-convex nonlinear feasibility problem. The first phase consists of four distinct algorithms facilitating mixed-integer linear, mixed-integer nonlinear, reduced nonlinear, and complementarity constrained methods to compute possible settings for the discrete decisions. The second phase employs a precise continuous nonlinear programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances whose size is significantly larger than networks that have appeared  in the literature previously.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-16531</identifier>
    <identifier type="doi">10.1080/10556788.2014.888426</identifier>
    <enrichment key="SourceTitle">Appeared in: Optimization Methods and Software 30 (2015) pp. 15-53</enrichment>
    <author>Marc Pfetsch</author>
    <submitter>Jonas Schweiger</submitter>
    <author>Armin Fügenschuh</author>
    <author>Björn Geißler</author>
    <author>Nina Geißler</author>
    <author>Ralf Gollmer</author>
    <author>Benjamin Hiller</author>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Alexander Martin</author>
    <author>Antonio Morsi</author>
    <author>Jessica Rövekamp</author>
    <author>Lars Schewe</author>
    <author>Martin Schmidt</author>
    <author>Rüdiger Schultz</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Claudia Stangl</author>
    <author>Marc Steinbach</author>
    <author>Stefan Vigerske</author>
    <author>Bernhard Willert</author>
    <series>
      <title>ZIB-Report</title>
      <number>12-41</number>
    </series>
    <collection role="ccs" number="D.">Software</collection>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="msc" number="90-08">Computational methods</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C30">Nonlinear programming</collection>
    <collection role="msc" number="90C90">Applications of mathematical programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="ForNe">ForNe</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="schweiger">Schweiger, Jonas</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1653/ZR-12-41.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/1653/ZR-12-41_revised.pdf</file>
  </doc>
  <doc>
    <id>1686</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2012-11-27</completedDate>
    <publishedDate>2012-11-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mixed-Integer Linear Methods for Layout-Optimization of Screening Systems in Recovered Paper Production</title>
    <abstract language="eng">The industrial treatment of waste paper in order to regain valuable&#13;
fibers from which recovered paper can be produced, involves several&#13;
steps of preparation. One important step is the separation of stickies&#13;
that are normally attached to the paper. If not properly separated,&#13;
remaining stickies reduce the quality of the recovered paper or even&#13;
disrupt the production process. For the mechanical separation process&#13;
of fibers from stickies a separator screen is used. This machine has&#13;
one input feed and two output streams, called the accept and the&#13;
reject. In the accept the fibers are concentrated, whereas the reject&#13;
has a higher concentration of stickies. The machine can be controlled&#13;
by setting its reject rate. But even when the reject rate is set&#13;
properly, after just a single screening step, the accept still has too&#13;
many stickies, or the reject too many fibers. To get a proper&#13;
separation, several separators have to be assembled into a&#13;
network. From a mathematical point of view this problem can be seen as&#13;
a multi-commodity network flow design problem with a nonlinear,&#13;
controllable distribution function at each node. We present a&#13;
nonlinear mixed-integer programming model for the simultaneous&#13;
selection of a subset of separators, the network's topology, and the&#13;
optimal setting of each separator. &#13;
Numerical results are obtained via &#13;
different types of linearization of the nonlinearities and the use of&#13;
mixed-integer linear solvers, and compared with state-of-the-art&#13;
global optimization software.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-16862</identifier>
    <author>Armin Fügenschuh</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Christine Hayn</author>
    <author>Dennis Michaels</author>
    <series>
      <title>ZIB-Report</title>
      <number>12-44</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-Integer Linear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nonlinear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Piecewise Linear Approximation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Topology Optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Network Design</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1686/ZR-12-44.pdf</file>
  </doc>
  <doc>
    <id>1570</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2012-08-07</completedDate>
    <publishedDate>2012-08-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Global Manufacturing: How to Use Mathematical Optimisation Methods to Transform to Sustainable Value Creation</title>
    <abstract language="eng">It is clear that a transformation to sustainable value creation is needed, because business as usual is not an option for preserving competitive advantages of leading industries. What does that mean? This contribution proposes possible approaches for a shift in existing manufacturing paradigms. In a first step, sustainability aspects from the German Sustainability Strategy and from the tools of life cycle sustainability assessment are chosen to match areas of a value creation process. Within these aspects are indicators, which can be measured within a manufacturing process. Once these data are obtained they can be used to set up a mathematical linear pulse model of manufacturing in order to analyse the evolution of the system over time, that is the transition process, by using a system dynamics approach. An increase of technology development by a factor of 2 leads to an increase of manufacturing but also to an increase of climate change. Compensation measures need to be taken. This can be done by e.g. taking money from the GDP (as an indicator of the aspect ``macroeconomic performance''). The value of the arc from that building block towards climate change must then be increased by a factor of 10. The choice of independent and representative indicators or aspects shall be validated and double-checked for their significance with the help of multi-criteria mixed-integer programming optimisation methods.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-15703</identifier>
    <enrichment key="SourceTitle">Appeared in: Proceedings of the 10th Global Conference on Sustainable Manufacturing (GCSM2012), Günther Seliger, S. Engin Kilic (Hrsg.), pp. 538 - 545, 2012  ISBN-978-605-63463-1-6</enrichment>
    <author>Rene Scheumann</author>
    <submitter>Ralf Borndörfer</submitter>
    <author>Armin Fügenschuh</author>
    <author>Sebastian Schenker</author>
    <author>Ingmar Vierhaus</author>
    <author>Ralf Borndörfer</author>
    <author>Matthias Finkbeiner</author>
    <series>
      <title>ZIB-Report</title>
      <number>12-28</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-Criteria Optimisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainability Indicator</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>System Dynamics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Value Creation Process</value>
    </subject>
    <collection role="ccs" number="G.">Mathematics of Computing</collection>
    <collection role="pacs" number="00.00.00">GENERAL</collection>
    <collection role="msc" number="90B30">Production models</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/1570/ScheumannFuegenschuhSchenkerVierhausBorndoerferFinkbeiner2012__global_manufacturing.pdf</file>
  </doc>
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