This research evaluates data-driven demand models using natural language processing techniques and analytical approaches. The first essay offers a comprehensive review of data-driven newsvendor literature and applies natural language processing techniques, including latent semantic analysis, latent Dirichlet allocation and cluster analysis to analyze the text data. This study highlights emerging trends and future research directions in the field of data-driven newsvendor research. The second essay contributes to the data-driven newsvendor inventory management literature by proposing nonparametric approaches that include Tobit and quantile regression incorporating leverage values under conditions of homogeneity and heterogeneity. Lastly, the third essay addresses the optimization of healthcare facility location and resource allocation in post-earthquake scenarios, presenting a linear programming model with telemedicine integration for effective disaster response. This study applies the model to the 2005 Kashmir earthquake in Pakistan. These essays collectively highlight the potential of data-driven methodologies in enhancing decision-making processes across diverse domains, while also pointing towards future research directions to address inherent complexities and uncertainties of the models.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2356171 |
| Date | 07 1900 |
| Creators | Gulzari, Adeela |
| Contributors | Pavur, Robert, Tarakci, Hakan, Rubio-Herrero, Javier, Torres, Russell |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Gulzari, Adeela, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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