<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1234</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2011-03-15</completedDate>
    <publishedDate>2011-03-15</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gas Network Topology Optimization for Upcoming Market Requirements</title>
    <abstract language="eng">Gas distribution networks are complex structures that consist of&#13;
passive pipes, and active, controllable elements such as valves and&#13;
compressors. Controlling such network means to find a suitable setting&#13;
for all active components such that a nominated amount of gas can be&#13;
transmitted from entries to exits through the network, without&#13;
violating physical or operational constraints. The control of a&#13;
large-scale gas network is a challenging task from a practical point&#13;
of view. In most companies the actual controlling process is supported&#13;
by means of computer software that is able to simulate the flow of the&#13;
gas. However, the active settings have to be set manually within such&#13;
simulation software. The solution quality thus depends on the&#13;
experience of a human planner.&#13;
&#13;
When the gas network is insufficient for the transport then topology&#13;
extensions come into play. Here a set of new pipes or active elements&#13;
is determined such that the extended network admits a feasible control&#13;
again. The question again is how to select these extensions and where&#13;
to place them such that the total extension costs are&#13;
minimal. Industrial practice is again to use the same simulation&#13;
software, determine extensions by experience, add them to the virtual&#13;
network, and then try to find a feasible control of the active&#13;
elements. The validity of this approach now depends even more on the&#13;
human planner.&#13;
&#13;
Another weakness of this manual simulation-based approach is that it&#13;
cannot establish infeasibility of a certain gas nomination, unless all&#13;
settings of the active elements are tried. Moreover, it is impossible&#13;
to find a cost-optimal network extension in this way.&#13;
In order to overcome these shortcomings of the manual planning&#13;
approach we present a new approach, rigorously based on mathematical&#13;
optimization. Hereto we describe a model for finding feasible&#13;
controls and then extend this model such that topology extensions can&#13;
additionally and simultaneously be covered. Numerical results for real-world instances are presented and&#13;
discussed.</abstract>
    <identifier type="serial">11-09</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-12348</identifier>
    <identifier type="doi">10.1109/EEM.2011.5953035</identifier>
    <enrichment key="SourceTitle">Appeared in: Engery Market (EEM), 2011 8th International Conference on the European, Zagreb, 25-27 May 2011, pp. 346-351</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter>-empty- (Opus4 user: )</submitter>
    <author>Benjamin Hiller</author>
    <submitter>Jonas Schweiger</submitter>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Jácint Szabó</author>
    <series>
      <title>ZIB-Report</title>
      <number>11-09</number>
    </series>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Mathematical Optimization</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Gas Distribution Networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Topology Planning</value>
    </subject>
    <collection role="msc" number="76N25">Flow control and optimization</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="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/1234/ZR-11-09.pdf</file>
  </doc>
  <doc>
    <id>4832</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>346</pageFirst>
    <pageLast>351</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gas Network Topology Optimization for Upcoming Market Requirements</title>
    <abstract language="eng">Gas distribution networks are complex structures that consist of passive pipes, and active, controllable elements such as valves and compressors. Controlling such network means to find a suitable setting for all active components such that a nominated amount of gas can be transmitted from entries to exits through the network, without violating physical or operational constraints. The control of a large-scale gas network is a challenging task from a practical point of view. In most companies the actual controlling process is supported by means of computer software that is able to simulate the flow of the gas. However, the active settings have to be set manually within such simulation software. The solution quality thus depends on the experience of a human planner. When the gas network is insufficient for the transport then topology extensions come into play. Here a set of new pipes or active elements is determined such that the extended network admits a feasible control again. The question again is how to select these extensions and where to place them such that the total extension costs are minimal. Industrial practice is again to use the same simulation software, determine extensions by experience, add them to the virtual network, and then try to find a feasible control of the active elements. The validity of this approach now depends even more on the human planner. Another weakness of this manual simulation-based approach is that it cannot establish infeasibility of a certain gas nomination, unless all settings of the active elements are tried. Moreover, it is impossible to find a cost-optimal network extension in this way. In order to overcome these shortcomings of the manual planning approach we present a new approach, rigorously based on mathematical optimization. Hereto we describe a model for finding feasible controls and then extend this model such that topology extensions can additionally and simultaneously be covered. Numerical results for real-world instances are presented and discussed.</abstract>
    <parentTitle language="eng">International Conference on the European Energy Market (EEM)</parentTitle>
    <identifier type="doi">10.1109/EEM.2011.5953035</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-12348</enrichment>
    <submitter>Jonas Schweiger</submitter>
    <author>Armin Fügenschuh</author>
    <author>Benjamin Hiller</author>
    <author>Jesco Humpola</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <author>Jacint Szabo</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</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>
  </doc>
  <doc>
    <id>4738</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>449</pageFirst>
    <pageLast>473</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName>Springer Berlin Heidelberg</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</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. While previously network operator and gas vendor were united, they were forced to split up into independent companies. 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. We discuss how these changing paradigms lead to new and challenging mathematical optimization problems. This includes the validation of nominations, that asks for the decision if the network’s capacity is sufficient to transport a specific amount of flow, the verification of booked capacities and the detection of available freely allocable capacities, and the topological extension of the network with new pipelines or compressors in order to increase its capacity. In order to solve each of these problems and to provide meaningful results for the practice, a mixture of different mathematical aspects have to be addressed, such as combinatorics, stochasticity, uncertainty, and nonlinearity. Currently, no numerical solver is available that can deal with such blended problems out-of-the-box. The main goal of our research is to develop such a solver, that moreover is able to solve instances of realistic size. In this article, we describe the main ingredients of our prototypical software implementations.</abstract>
    <parentTitle language="eng">Energy Systems</parentTitle>
    <identifier type="doi">10.1007/s12667-013-0099-8</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-17821</enrichment>
    <author>Armin Fügenschuh</author>
    <submitter> Humpola</submitter>
    <author>Björn Geißler</author>
    <author>Ralf Gollmer</author>
    <author>Christine Hayn</author>
    <author>René 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>
    <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>
  </doc>
  <doc>
    <id>4311</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-11-21</completedDate>
    <publishedDate>2013-11-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Primal Heuristic for MINLP based on Dual Information</title>
    <abstract language="eng">We present a novel heuristic algorithm to identify feasible solutions of a mixed-integer nonlinear programming problem arising in natural gas transportation: the selection of new pipelines to enhance the network's capacity to a desired level in a cost-efficient way. We solve this problem in a linear programming based branch-and-cut approach, where we deal with the nonlinearities by linear outer approximation and spatial branching. At certain nodes of the branching tree, we compute a KKT point for a nonlinear relaxation. Based on the information from the KKT point we alter some of the integer variables in a locally promising way. We describe this heuristic for general MINLPs and then show how to tailor the heuristic to exploit our problem-specific structure. On a test set of real-world instances, we are able to increase the chance of identifying feasible solutions by some order of magnitude compared to standard MINLP heuristics that are already built in the general-purpose MINLP solver SCIP.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-43110</identifier>
    <identifier type="doi">10.1007/s13675-014-0029-0</identifier>
    <enrichment key="SourceTitle">Appeared under the title "A primal heuristic for optimizing the topology of gas networks based on dual information" in: EURO Journal on Computational Optimization 2014</enrichment>
    <author>Jesco Humpola</author>
    <submitter>Armin Fügenschuh</submitter>
    <author>Armin Fügenschuh</author>
    <author>Thomas Lehmann</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-49</number>
    </series>
    <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>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Heuristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Duality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nonlinear Network Design Applications</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/4311/ZR-13-49.pdf</file>
  </doc>
  <doc>
    <id>5376</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>597</pageFirst>
    <pageLast>631</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>38</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Valid inequalities for the topology optimization problem in gas network design</title>
    <abstract language="eng">One quarter of Europe's energy demand is provided by natural gas distributed through a vast pipeline network covering the whole of Europe. At a cost of 1 million Euro per km extending the European pipeline network is already a multi-billion Euro business. Therefore, automatic planning tools that support the decision process are desired. Unfortunately, current mathematical methods are not capable of solving the arising network design problems due to their size and complexity. In this article, we will show how to apply optimization methods that can converge to a proven global optimal solution. By introducing a new class of valid inequalities that improve the relaxation of our mixed-integer nonlinear programming model, we are able to speed up the necessary computations substantially.</abstract>
    <parentTitle language="eng">OR Spectrum</parentTitle>
    <identifier type="doi">https://doi.org/10.1007/s00291-015-0390-2</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-17771</enrichment>
    <author>Jesco Humpola</author>
    <submitter>Jesco Humpola</submitter>
    <author>Armin Fügenschuh</author>
    <author>Thorsten Koch</author>
    <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="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>5467</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>717</pageFirst>
    <pageLast>759</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>62</volume>
    <type>article</type>
    <publisherName>Springer US</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Convex reformulations for solving a nonlinear network design problem</title>
    <abstract language="eng">We consider a nonlinear nonconvex network design problem that arises, for example, in natural gas or water transmission networks. Given is such a network with active and passive components, that is, valves, compressors, control valves (active) and pipelines (passive), and a desired amount of flow at certain specified entry and exit nodes in the network. The active elements are associated with costs when used. 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 to compute a cost minimal setting of the active components and numerical values for the flow and node potentials. We examine different (convex) relaxations for a subproblem of the design problem and benefit from them within a branch-and-bound approach. We compare different approaches based on nonlinear optimization numerically on a set of test instances.</abstract>
    <parentTitle language="eng">Computational Optimization and Applications</parentTitle>
    <identifier type="doi">10.1007/s10589-015-9756-2</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-18857</enrichment>
    <author>Jesco Humpola</author>
    <submitter>Jesco Humpola</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="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>
  </doc>
  <doc>
    <id>5345</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>SIAM-MOS series on Optimization</volume>
    <type>bookpart</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Specialized MINLP Approach</title>
    <abstract language="eng">We propose an approach to solve the validation of nominations problem using mixed-integer nonlinear programming (MINLP) methods. Our approach handles both the discrete settings and the nonlinear aspects of gas physics. Our main contribution is an innovative coupling of mixed-integer (linear) programming (MILP) methods with nonlinear programming (NLP) that exploits the special structure of a suitable approximation of gas physics, resulting in a global optimization method for this type of problem.</abstract>
    <parentTitle language="eng">Evaluating Gas Network Capacities</parentTitle>
    <identifier type="isbn">9781611973686</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Jesco Humpola</author>
    <submitter>Jesco Humpola</submitter>
    <author>Armin Fügenschuh</author>
    <author>Benjamin Hiller</author>
    <author>Thorsten Koch</author>
    <author>Thomas Lehmann</author>
    <author>Ralf Lenz</author>
    <author>Robert Schwarz</author>
    <author>Jonas Schweiger</author>
    <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="lenz">Lenz, Ralf</collection>
    <collection role="projects" number="MODAL-GasLab">MODAL-GasLab</collection>
    <collection role="projects" number="ForNe">ForNe</collection>
    <collection role="projects" number="TRR154-A04">TRR154-A04</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>
  </doc>
  <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>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>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>5249</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>53</pageFirst>
    <pageLast>78</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>3</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A primal heuristic for optimizing the topology of gas networks based on dual information</title>
    <abstract language="eng">We present a novel heuristic to identify feasible solutions of a mixed-integer nonlinear programming problem arising in natural gas transportation: the selection of new pipelines to enhance the network's capacity to a desired level in a cost-efficient way. We solve this problem in a linear programming based branch-and-cut approach, where we deal with the nonlinearities by linear outer approximation and spatial branching. At certain nodes of the branching tree, we compute a KKT point of a nonlinear relaxation. Based on the information from the KKT point we alter some of the binary variables in a locally promising way exploiting our problem-specific structure. On a test set of real-world instances, we are able to increase the chance of identifying feasible solutions by some order of magnitude compared to standard MINLP heuristics that are already built in the general-purpose MINLP solver SCIP.</abstract>
    <parentTitle language="eng">EURO Journal on Computational Optimization</parentTitle>
    <identifier type="doi">10.1007/s13675-014-0029-0</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-43110</enrichment>
    <author>Jesco Humpola</author>
    <submitter>Jesco Humpola</submitter>
    <author>Thomas Lehmann</author>
    <author>Armin Fügenschuh</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</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>
  </doc>
</export-example>
