Abstract
Ranking methods are an essential tool to help make decisions. This dissertation document examines different aspects of the theory and application of pairwise comparison ranking methods, specifically those that use Markov chains. First, a new method is developed to solve a traditional recruiting problem, and is shown to improve the predictive power of its ranking. Next, modifications are made to an existing method that theoretically improves the reliability, while maintaining the rank integrity. Last, a framework is developed that defines a fair and comprehensive ranking method, and several popular methods are evaluated in their ability to adhere to the said framework.
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Industrial Engineering
Committee Chair
Yuehwern Yih
Committee Co-Chair
Tom Morin
Date of Award
5-2016
Recommended Citation
Vaziri, Baback, "Markov-based ranking methods" (2016). Open Access Dissertations. 721.
https://docs.lib.purdue.edu/open_access_dissertations/721
First Advisor
Yuehwern Yih
Second Advisor
Tom Morin
Committee Member 1
Mark Lehto
Committee Member 2
Robert Plante