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  <title><![CDATA[ISyE Seminar -  Prof. J. Cole Smith]]></title>
  <body><![CDATA[<h3>Title:</h3>

<p>Asymmetric Stochastic Shortest-Path Interdiction Favoring the Evader</p>

<h3>&nbsp;<br />
Abstract:&nbsp;</h3>

<h3>This work was completed with Dr. Di Nguyen, a professor at University College Dublin. We discuss a two-stage shortest-path interdiction problem between an interdictor and&nbsp;an evader, in which the cost for an evader to use each arc is given by the arc&rsquo;s&nbsp;base&nbsp;cost plus an additional cost if the arc is attacked by the interdictor. The interdictor&nbsp;acts first to attack a subset of arcs, and&nbsp;then the evader traverses the network using a&nbsp;shortest path. In the problem we study, the interdictor does not know the exact&nbsp;value of&nbsp;each base cost, but instead only knows the (nonnegative uniform) distributions of each&nbsp;arc&rsquo;s base cost. The evader&nbsp;observes both the subset of arcs attacked by the interdictor&nbsp;and the true base cost values before traversing the network, and is&nbsp;thus at an advantage. The interdictor seeks to&nbsp;maximize evader&rsquo;s shortest-path costs, but the choice of objective is a key&nbsp;consideration. We examine ideas underscoring how the interdictor could maximize the expected objective that an evader will incur,&nbsp;and then more generally explore the maximization of the evader&rsquo;s conditional value-at-risk, given some specified risk parameter.&nbsp;<br />
&nbsp;<br />
Bio sketch:</h3>

<p>&nbsp;Dr. J. Cole Smith is&nbsp;Dean&nbsp;of the College of Engineering and Computer Science at Syracuse University. Prior to that role,&nbsp;he served as an Associate Provost for Academic Initiatives and as Department Chair of Industrial Engineering at Clemson University.&nbsp;His research regards mathematical optimization models and algorithms, especially those arising in combinatorial optimization. Dr.&nbsp;Smith&rsquo;s awards include the Young Investigator Award from the ONR, the Hamid K. Elden Outstanding Young Industrial Engineer in&nbsp;Education award, the Operations Research Division Teaching Award, the 2014 Glover-Klingman prize for best paper in Networks,&nbsp;and the best paper award from IIE Transactions in 2007. He became a Fellow of IISE in 2018, and serves as the INFORMS Vice&nbsp;President of Publications.</p>

<p>&nbsp;</p>
]]></body>
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      <value><![CDATA[Asymmetric Stochastic Shortest-Path Interdiction Favoring the Evader]]></value>
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      <value><![CDATA[<h3><strong>Abstract:&nbsp;</strong></h3>

<p>This work was completed with Dr. Di Nguyen, a professor at University College Dublin. We discuss a two-stage shortest-path interdiction problem between an interdictor and&nbsp;an evader, in which the cost for an evader to use each arc is given by the arc&rsquo;s&nbsp;base&nbsp;cost plus an additional cost if the arc is attacked by the interdictor. The interdictor&nbsp;acts first to attack a subset of arcs, and&nbsp;then the evader traverses the network using a&nbsp;shortest path. In the problem we study, the interdictor does not know the exact&nbsp;value of&nbsp;each base cost, but instead only knows the (nonnegative uniform) distributions of each&nbsp;arc&rsquo;s base cost. The evader&nbsp;observes both the subset of arcs attacked by the interdictor&nbsp;and the true base cost values before traversing the network, and is&nbsp;thus at an advantage. The interdictor seeks to&nbsp;maximize evader&rsquo;s shortest-path costs, but the choice of objective is a key&nbsp;consideration. We examine ideas underscoring how the interdictor could maximize the expected objective that an evader will incur,&nbsp;and then more generally explore the maximization of the evader&rsquo;s conditional value-at-risk, given some specified risk parameter.&nbsp;<br />
<br />
&nbsp;</p>
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