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  <doc>
    <id>1815</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">A System Dynamic Enhancement for the Scenario Technique</title>
    <abstract language="eng">The Scenario Technique is a strategic planning method that aims to describe and analyze potential developments of a considered system in the future. Its application consists of several steps, from an initial problem analysis over an influence analysis to projections of key factors and a definition of the scenarios to a final interpretation of the results. The technique itself combines qualitative and quantitative methods and is an enhancement of the standard Scenario Technique. We use the numerical values gathered during the influence analysis, and embed them in a System Dynamics framework. This yields a mathematically rigorous way to achieve predictions of the system‘s future behavior from an initial impulse and the feedback structure of the factors. The outcome of our new method is a further way of projecting the present into the future, which enables the user of the Scenario Technique to obtain a validation of the results achieved by the standard method.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-18150</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 613 -- 618</enrichment>
    <author>Achim Brose</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Armin Fügenschuh</author>
    <author>Pia Gausemeier</author>
    <author>Ingmar Vierhaus</author>
    <author>Günther Seliger</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-24</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scenario Technique; System Dynamics</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/1815/ZR-13-24.pdf</file>
  </doc>
  <doc>
    <id>4822</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>536</pageFirst>
    <pageLast>541</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Universitätsverlag der TU Berlin</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</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>
    <parentTitle language="eng">11th Global Conference on Sustainable Manufacturing : Proceedings</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-18163</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Roel van Veldhuizen</author>
    <author>Ingmar Vierhaus</author>
    <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>
  </doc>
  <doc>
    <id>4823</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>530</pageFirst>
    <pageLast>535</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Universitätsverlag der TU Berlin</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</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 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 nonlinear. Therefore seemingly simple systems can show a nonintuitive, nonpredictable behavior over time. Controlling a dynamical system means to define a desired final state in which the system should be, and to specify potential interventions from outside that should keep the system on the right track. The central question is how to compute such globally optimal control for a given SD model. We propose a branch-and-bound approach that is based on a bound propagation method, primal heuristics, and spatial branching. We apply our new SD-control method to a small System Dynamics model, that describes the evolution of a social-economic system over time. We examine the problem of steering this system on a sustainable consumption path.</abstract>
    <parentTitle language="eng">11th Global Conference on Sustainable Manufacturing : Proceedings</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-18148</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Ingmar Vierhaus</author>
    <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>
  </doc>
  <doc>
    <id>4816</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>561</pageFirst>
    <pageLast>566</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Universitätsverlag der TU Berlin</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A System Dynamic Enhancement for the Scenario Technique</title>
    <abstract language="eng">The Scenario Technique is a strategic planning method that aims to describe and analyze potential developments of a considered system in the future. Its application consists of several steps, from an initial problem analysis over an influence analysis to projections of key factors and a definition of the scenarios to a final interpretation of the results. The technique itself combines qualitative and quantitative methods and is an enhancement of the standard Scenario Technique. We use the numerical values gathered during the influence analysis, and embed them in a System Dynamics framework. This yields a mathematically rigorous way to achieve predictions of the system‘s future behavior from an initial impulse and the feedback structure of the factors. The outcome of our new method is a further way of projecting the present into the future, which enables the user of the Scenario Technique to obtain a validation of the results achieved by the standard method.</abstract>
    <parentTitle language="eng">Proc. 11th Global Conference on Sustainable Manufacturing</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-18150</enrichment>
    <author>Achim Brose</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Armin Fügenschuh</author>
    <author>Pia Gausemeier</author>
    <author>Ingmar Vierhaus</author>
    <author>Günther Seliger</author>
    <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>
  </doc>
  <doc>
    <id>4817</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>523</pageFirst>
    <pageLast>531</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</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>
    <parentTitle language="eng">Proceedings of the 27. Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management</parentTitle>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-18895</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>René Scheumann</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Ingmar Vierhaus</author>
    <author>Ya-Ju Chang</author>
    <author>Armin Fügenschuh</author>
    <author>Matthias Finkbeiner</author>
    <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>
  </doc>
  <doc>
    <id>4800</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>538</pageFirst>
    <pageLast>545</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Global Manufacturing: How to Use Mathematical Optimisation Methods to Transform to Sustainable Value Creation</title>
    <parentTitle language="eng">Proceedings of the 10th Global Conference on Sustainable Manufacturing</parentTitle>
    <subTitle language="eng">(GCSM 2012)</subTitle>
    <identifier type="isbn">978-605-63463-1-6</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-15703</enrichment>
    <author>René Scheumann</author>
    <submitter>Sebastian Schenker</submitter>
    <editor>Günther Seliger</editor>
    <author>Armin Fügenschuh</author>
    <author>Sebastian Schenker</author>
    <author>Ingmar Vierhaus</author>
    <author>Ralf Borndörfer</author>
    <author>Matthias Finkbeiner</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="traffic">Mathematics of Transportation and Logistics</collection>
    <collection role="persons" number="borndoerfer">Borndörfer, Ralf</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="projects" number="CRC1026">CRC1026</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>4815</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2014-03-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modern Nonlinear Optimization Techniques for an Optimal Control of System Dynamics Models</title>
    <abstract language="eng">We study System Dynamics models with several free parameters that can be altered by the user. We assume that the user's goal is to achieve a certain dynamic behavior of the model by varying these parameters. In order to the find best possible combination of parameter settings, several automatic parameter tuning methods are described in the literature and readily available within existing System Dynamic software packages. We give a survey on the available techniques in the market and describe their theoretical background. Some of these methods are already six decades old, and meanwhile newer and more powerful optimization methods have emerged in the mathematical literature. One major obstacle for their direct use are tabled data in System Dynamics models, which are usually interpreted as piecewise linear functions. However, modern optimization methods usually require smooth functions which are twice continuously differentiable. We overcome this problem by a smooth spline interpolation of the tabled data. We use a test set of three complex System Dynamic models from the literature, describe their individual transition into optimization problems, and demonstrate the applicability of modern optimization algorithms to these System Dynamics Optimization problems.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-48159</identifier>
    <author>Ingmar Vierhaus</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Armin Fügenschuh</author>
    <author>Robert Lion Gottwald</author>
    <author>Stefan N. Grösser</author>
    <series>
      <title>ZIB-Report</title>
      <number>14-08</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>System Dynamics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimal Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nonlinear Optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spline Interpolation</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="49-XX">CALCULUS OF VARIATIONS AND OPTIMAL CONTROL; OPTIMIZATION [See also 34H05, 34K35, 65Kxx, 90Cxx, 93-XX]</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <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="robert.gottwald">Gottwald, Robert</collection>
    <collection role="projects" number="CRC1026">CRC1026</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/4815/ZR-14-08.pdf</file>
  </doc>
  <doc>
    <id>4293</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-10-22</completedDate>
    <publishedDate>2013-10-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Global Approach to the Control of an Industry Structure System Dynamics Model</title>
    <abstract language="eng">We consider a system dynamics model that describes the effect of human activity on natural resources. The central stocks are the accumulated profit, the industry structures, and the water resources. The model can be controlled through two time-dependent parameters. The goal in this paper is to find a parameter setting that leads to a maximization of a performance index, which reflects both environmental and economic aspects. Thus, the goal is to identify the most sustainable stock of industry structures within the model's constraints and assumptions. In order to find a proven global optimal parameter set, we formulate the System Dynamics Optimization model as a mixed-integer nonlinear problem that is accessible for numerical solvers. Due to the dynamic structure of the model, certain steps of the solution process must be handled with greater care, compared to standard non-dynamic problems. We describe our approach of solving the industry structure model and present computational results. In addition, we discuss the limitations of the approach and next steps.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-42932</identifier>
    <author>Armin Fügenschuh</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Stefan N. Grösser</author>
    <author>Ingmar Vierhaus</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-67</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>System Dynamics; Mixed-Integer Nonlinear Optimization</value>
    </subject>
    <collection role="ccs" number="G.">Mathematics of Computing</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="msc" number="93-XX">SYSTEMS THEORY; CONTROL (For optimal control, see 49-XX)</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/4293/ZR-13-67.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>
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  <doc>
    <id>6274</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Global and Local Optimal Control of a Resource Utilization Problem</title>
    <abstract language="eng">System Dynamic models describe physical, technical, economical, or social systems using differential and algebraic equations. In their purest form, these models are intended to describe the evolution of a system from a given initial state. In many applications, it is possible to intervene with the system in order to obtain a desired dynamic or a certain outcome in the end. On the mathematical side, this leads to control problems, where aside from the simulation one has to find optimal intervention functions over time that maximize a specific objective function. Using a dynamical model for the utilization of a natural nonrenewable resource of Behrens as a demonstrator example, we present two main mathematical solution strategies.  They are distinguished by the quality certificate on their respective solution: one leads to proven local optimal solution, and the other technique yields proven global optimal solutions. We present implementational and numerical issues, and a comparison of both methods.</abstract>
    <parentTitle language="eng">Proceedings of the&#13;
33rd International Conference of the System Dynamics Society</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="FulltextUrl">http://www.systemdynamics.org/conferences/2015/proceed/papers/P1376.pdf</enrichment>
    <author>Ingmar Vierhaus</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Armin Fügenschuh</author>
    <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>
  </doc>
  <doc>
    <id>6188</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>239</pageFirst>
    <pageLast>253</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimisation Methods in Sustainable Manufacturing</title>
    <abstract language="eng">Sustainable manufacturing is driven by the insight that the focus on the economic dimension in current businesses and lifestyles has to be broadened to cover all three pillars of sustainability: economic development, social development, and environmental protection.</abstract>
    <parentTitle language="eng">Sustainable Manufacturing</parentTitle>
    <subTitle language="eng">Challenges, Solutions and Implementation Perspectives</subTitle>
    <identifier type="isbn">978-3-319-48514-0</identifier>
    <identifier type="doi">10.1007/978-3-319-48514-0_15</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Sebastian Schenker</author>
    <submitter>Sebastian Schenker</submitter>
    <editor>Rainer Stark</editor>
    <author>Ingmar Vierhaus</author>
    <editor>Günther Seliger</editor>
    <author>Ralf Borndörfer</author>
    <editor>Jérémy Bonvoisin</editor>
    <author>Armin Fügenschuh</author>
    <author>Martin Skutella</author>
    <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="projects" number="MODAL-RailLab">MODAL-RailLab</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="CRC1026">CRC1026</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>5302</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</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. 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. Numerical results for these test cases are presented.</abstract>
    <parentTitle language="eng">Proceedings of the 31st International Conference of the System Dynamics Society</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-18600</enrichment>
    <author>Ingmar Vierhaus</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Armin Fügenschuh</author>
    <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>
  </doc>
  <doc>
    <id>5303</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modern Nonlinear Optimization Techniques for an Optimal Control of System Dynamics Models</title>
    <abstract language="eng">We study System Dynamics models with several free parameters that can be altered by the user. We assume that the user's goal is to achieve a certain dynamic behavior of the model by varying these parameters. In order to find best possible combination of parameter settings, several automatic parameter tuning methods are described in the literature and readily available within existing System Dynamic software packages. We give a survey on the available techniques in the market and describe their theoretical background. Some of these methods are already six decades old, and meanwhile newer and more powerful optimization methods have emerged in the mathematical literature. One major obstacle for their direct use are tabled data in System Dynamics models, which are usually interpreted as piecewise linear functions. However, modern optimization methods usually require smooth functions which are twice continuously differentiable. We overcome this problem by a smooth spline interpolation of the tabled data. We use a test set of three complex System Dynamic models from the literature, describe their individual transition into optimization problems, and demonstrate the applicability of modern optimization algorithms to these System Dynamics Optimization problems.</abstract>
    <parentTitle language="eng">Proceedings of the 32nd International Conference of the System Dynamics Society</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-48159</enrichment>
    <author>Ingmar Vierhaus</author>
    <submitter>Ingmar Vierhaus</submitter>
    <author>Armin Fügenschuh</author>
    <author>Robert Lion Gottwald</author>
    <author>Stefan Grösser</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="fuegenschuh">Fügenschuh, Armin</collection>
    <collection role="persons" number="robert.gottwald">Gottwald, Robert</collection>
    <collection role="projects" number="CRC1026">CRC1026</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
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