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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Vaziri, Baback"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Yuehwern Yih","Tom Morin","Mark Lehto","Robert Plante"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-05-01T07:00:00Z","date_published":"2016-05-01T07:00:00Z","updated_at":"2026-07-24T03:53:55Z","subjects":["Applied sciences","Markov chains","Ranking methods","Industrial Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://docs.lib.purdue.edu/open_access_dissertations/721","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yuehwern Yih","Tom Morin","Mark Lehto","Robert Plante"]},{"key":"dc:creator","label":"Author","values":["Vaziri, Baback"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied sciences","Markov chains","Ranking methods","Industrial Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://docs.lib.purdue.edu/open_access_dissertations/721"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
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