ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.
In order to address concerns about the dominance of petroleum-fueled vehicles, the transition to alternative-fueled counterparts is urgently needed. Top barriers preventing the widespread diffusion of alternative-fuel vehicles (AFV) are the limited range and the scarcity of refueling or recharging infrastructures in convenient locations. Researchers have been developing models for optimally locating refueling facilities for range-limited vehicles, and recently a strategy has emerged to cluster refueling stations to encourage consumers to purchase alternative-fuel vehicles by building a critical mass of stations. However, clustering approaches have not yet been developed based on flow-based demand. This study proposes a Threshold Coverage extension …
- Hong, Shuyao, Kuby, Michael J, Parker, Nathan C, et al.
- Created Date
Facility location models are usually employed to assist decision processes in urban and regional planning. The focus of this research is extensions of a classic location problem, the Weber problem, to address continuously distributed demand as well as multiple facilities. Addressing continuous demand and multi-facilities represents major challenges. Given advances in geographic information systems (GIS), computational science and associated technologies, spatial optimization provides a possibility for improved problem solution. Essential here is how to represent facilities and demand in geographic space. In one respect, spatial abstraction as discrete points is generally assumed as it simplifies model formulation and reduces computational …
- Yao, Jing, Murray, Alan T, Mirchandani, Pitu B, et al.
- Created Date