<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>141</id>
    <completedYear>2017</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>443</pageFirst>
    <pageLast>460</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-05-16</completedDate>
    <publishedDate>2017-05-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A joint model of probabilistic/robust constraints for gas transport management in stationary networks</title>
    <abstract language="eng">We present a novel mathematical algorithm to assist gas network operators in managing uncertainty,&#13;
while increasing reliability of transmission and supply. As a result, we solve an optimization problem&#13;
with a joint probabilistic constraint over an infinite system of random inequalities. Such models arise&#13;
in the presence of uncertain parameters having partially stochastic and partially non-stochastic character.&#13;
The application that drives this new approach is a stationary network with uncertain demand&#13;
(which are stochastic due to the possibility of fitting statistical distributions based on historical measurements)&#13;
and with uncertain roughness coefficients in the pipes (which are uncertain but non-stochastic due to a lack of&#13;
attainable measurements).&#13;
We study the sensitivity of local uncertainties in the roughness coefficients and their impact on a highly reliable&#13;
network operation. In particular, we are going to answer the question, what is the maximum uncertainty that is&#13;
allowed (shaping a 'maximal' uncertainty set) around nominal roughness coefficients, such that random demands in&#13;
a stationary gas network can be satisfied at given high probability level for no matter which realization of&#13;
true roughness coefficients within the uncertainty set.&#13;
One ends up with a constraint, which is probabilistic with respect to the load of gas&#13;
and robust with respect to the roughness coefficients. We demonstrate how such constraints can be dealt with in&#13;
the framework of the so-called spheric-radial decomposition of multivariate Gaussian distributions.&#13;
The numerical solution of a corresponding optimization problem is illustrated.&#13;
The results might assist the network operator with the implementation&#13;
of cost-intensive roughness measurements.</abstract>
    <parentTitle language="eng">Computational Management Science</parentTitle>
    <identifier type="doi">10.1007/s10287-017-0284-7</identifier>
    <enrichment key="SubmissionStatus">in press</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Tatiana Gonzalez Grandon</author>
    <author>Holger Heitsch</author>
    <author>Rene Henrion</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>chance constraint</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>robust constraint</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>uncertainty set</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>spheric-radial decomposition</value>
    </subject>
    <collection role="institutes" number="">Humboldt-Universität zu Berlin</collection>
    <collection role="institutes" number="">Weierstraß-Institut für Angewandte Analysis und Stochastik</collection>
    <collection role="subprojects" number="">B04</collection>
    <collection role="subprojects" number="">Z01</collection>
  </doc>
</export-example>
