Seminar by Banu Lokman

Banu Lokman

Finding nondominated points for multi-objective integer programs and its applications

Seminar by
Banu Lokman
Department of Industrial Engineering
Middle East Technical University

Finding nondominated points is typically hard for Multi-objective Integer Programs (MOIPs) and the number of nondominated points is large when the objectives are conflicting. It is not practical for a decision maker (DM) to compare all of these points to make a decision. Therefore, it is important to generate nondominated points in a region that is of interest to the DM. In this study, we first present two exact algorithms that efficiently generate all nondominated points for MOIPs. We then develop a variation of those algorithms that generates the true nondominated points in any specified region for MOIPs. To define the preferred region of the DM, we also develop a procedure that first approximates the nondominated set using a hypersurface, finds a preferred hypothetical point on this surface and then defines a preferred region around the hypothetical point. Once the preferred region is defined, all nondominated points in that region are generated. We test the performance of the algorithms on some multi-objective combinatorial optimization problems and demonstrate that the algorithms work well.
This is a joint work with Murat Köksalan.
Short Bio:
Banu Lokman is an Assistant Professor in the Industrial Engineering (IE) Department of Middle East Technical University (METU). After receiving her BS degree in 2005, she worked for ASELSAN as a planning engineer during 2005-2006. She received her MS degree in 2007 and her PhD degree in 2011 from the IE department of METU. She was a research assistant in the same department from 2006 to 2011. She joined TED University in 2012. From May 2012 to June 2013, she worked as a visiting researcher in School of Business of Aalto University, on leave from TED University. She has been a member of the faculty in IE Department of METU since September, 2014. Her current research interests are under the umbrella of multi-criteria decision making, in areas such as combinatorial optimization, evolutionary algorithms, and applications.
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