Event Details

• Date/Time:
  • Friday October 7, 2022
    11:30 am - 12:30 pm
• Location: Atlanta, GA
• Phone:
• URL: Main 228
• Email:
• Fee(s):
  N/A
• Extras:
  

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Summaries

Summary Sentence: Asymmetric Stochastic Shortest-Path Interdiction Favoring the Evader

Full Summary:

Abstract: 

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 an evader, in which the cost for an evader to use each arc is given by the arc’s base cost plus an additional cost if the arc is attacked by the interdictor. The interdictor acts first to attack a subset of arcs, and then the evader traverses the network using a shortest path. In the problem we study, the interdictor does not know the exact value of each base cost, but instead only knows the (nonnegative uniform) distributions of each arc’s base cost. The evader observes both the subset of arcs attacked by the interdictor and the true base cost values before traversing the network, and is thus at an advantage. The interdictor seeks to maximize evader’s shortest-path costs, but the choice of objective is a key consideration. We examine ideas underscoring how the interdictor could maximize the expected objective that an evader will incur, and then more generally explore the maximization of the evader’s conditional value-at-risk, given some specified risk parameter. 

 

Title:

Asymmetric Stochastic Shortest-Path Interdiction Favoring the Evader

 
Abstract: 

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 an evader, in which the cost for an evader to use each arc is given by the arc’s base cost plus an additional cost if the arc is attacked by the interdictor. The interdictor acts first to attack a subset of arcs, and then the evader traverses the network using a shortest path. In the problem we study, the interdictor does not know the exact value of each base cost, but instead only knows the (nonnegative uniform) distributions of each arc’s base cost. The evader observes both the subset of arcs attacked by the interdictor and the true base cost values before traversing the network, and is thus at an advantage. The interdictor seeks to maximize evader’s shortest-path costs, but the choice of objective is a key consideration. We examine ideas underscoring how the interdictor could maximize the expected objective that an evader will incur, and then more generally explore the maximization of the evader’s conditional value-at-risk, given some specified risk parameter. 
 
Bio sketch:

 Dr. J. Cole Smith is Dean of the College of Engineering and Computer Science at Syracuse University. Prior to that role, he served as an Associate Provost for Academic Initiatives and as Department Chair of Industrial Engineering at Clemson University. His research regards mathematical optimization models and algorithms, especially those arising in combinatorial optimization. Dr. Smith’s awards include the Young Investigator Award from the ONR, the Hamid K. Elden Outstanding Young Industrial Engineer in Education award, the Operations Research Division Teaching Award, the 2014 Glover-Klingman prize for best paper in Networks, and the best paper award from IIE Transactions in 2007. He became a Fellow of IISE in 2018, and serves as the INFORMS Vice President of Publications.

Additional Information

In Campus Calendar

Yes

Groups

School of Industrial and Systems Engineering (ISYE)

Invited Audience

Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students

Categories

Seminar/Lecture/Colloquium

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Status

• Created By: mwelch39
• Workflow Status: Published
• Created On: Oct 3, 2022 - 10:25am
• Last Updated: Oct 6, 2022 - 10:46am