New Approaches to Classical OR Problems (Time-Sensitive Applications)
Seminar by Bahar Çavdar Georgia Institute of Technology
Desautels Faculty of Management McGill University
In this talk, we present our work in modifying classical models to address modern computational trade-offs and behavioral observations. Most of this talk focuses on time-related issues: instead of following the conventional compute-first-implement- later approach, we develop methods to embed computation into implementation in time-sensitive applications of well-known models such as TSP and VRP. We demonstrate the effectiveness of our approach and the break-even analysis for its use. We also present our tangential work on a tour length estimation model for random graphs that compares favorably with the seminal result by Beardwood et al. (1959). Finally, we discuss the motivation for our research in updating classical models based on the results of recent behavioral studies. This is joint work with Prof. Joel Sokol. BIO Bahar Çavdar received her B.S. degree in Industrial Engineering with a minor in Computer Engineering from Middle East Technical University in 2009. She is currently a Ph.D. candidate at Georgia Institute of Technology. Her current research interests are in balancing computation and implementation time when solving real- time problems in time-sensitive applications and integrating human behavior into standard models.
