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You searched for +publisher:"University of Texas – Austin" +contributor:("Morton, David P."). Showing records 1 – 6 of 6 total matches.

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1. Lu, Fang, active 21st century. Modeling and optimization for spatial detection to minimize abandonment rate.

Degree: PhD, Operations Research & Industrial Engineering, 2014, University of Texas – Austin

 Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These… (more)

Subjects/Keywords: Spatial detection; Queues with abandonments; Simulation; Stochastic programming; Multi-stage stochastic facility location problem; Scheduling heuristics

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APA (6th Edition):

Lu, Fang, a. 2. c. (2014). Modeling and optimization for spatial detection to minimize abandonment rate. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/25998

Chicago Manual of Style (16th Edition):

Lu, Fang, active 21st century. “Modeling and optimization for spatial detection to minimize abandonment rate.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed February 24, 2020. http://hdl.handle.net/2152/25998.

MLA Handbook (7th Edition):

Lu, Fang, active 21st century. “Modeling and optimization for spatial detection to minimize abandonment rate.” 2014. Web. 24 Feb 2020.

Vancouver:

Lu, Fang a2c. Modeling and optimization for spatial detection to minimize abandonment rate. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/2152/25998.

Council of Science Editors:

Lu, Fang a2c. Modeling and optimization for spatial detection to minimize abandonment rate. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/25998


University of Texas – Austin

2. -3880-9783. Models and methods for operational planning in freight railroads.

Degree: PhD, Operations Research and Industrial Engineering, 2015, University of Texas – Austin

 Railroads are facing increasing demand for freight transportation. Effective planning and scheduling are crucial to improve the utilization of expensive resources (such as crew and… (more)

Subjects/Keywords: Optimization; Railroad operational planning

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APA (6th Edition):

-3880-9783. (2015). Models and methods for operational planning in freight railroads. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31604

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-3880-9783. “Models and methods for operational planning in freight railroads.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 24, 2020. http://hdl.handle.net/2152/31604.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-3880-9783. “Models and methods for operational planning in freight railroads.” 2015. Web. 24 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3880-9783. Models and methods for operational planning in freight railroads. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/2152/31604.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3880-9783. Models and methods for operational planning in freight railroads. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31604

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

3. -2601-7604. Decomposition and variance reduction techniques for stochastic mixed integer programs.

Degree: PhD, Operations research and industrial engineering, 2019, University of Texas – Austin

 Obtaining upper and lower bounds on the optimal value of a stochastic integer program can require solution of multiple-scenario problems, which are computationally expensive or… (more)

Subjects/Keywords: Mixed integer programming; Stochastic programming; Microgrid design optimization; Variance reduction; Latin hypercube sampling; Monte Carlo simulation

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APA (6th Edition):

-2601-7604. (2019). Decomposition and variance reduction techniques for stochastic mixed integer programs. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1058

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-2601-7604. “Decomposition and variance reduction techniques for stochastic mixed integer programs.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed February 24, 2020. http://dx.doi.org/10.26153/tsw/1058.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-2601-7604. “Decomposition and variance reduction techniques for stochastic mixed integer programs.” 2019. Web. 24 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-2601-7604. Decomposition and variance reduction techniques for stochastic mixed integer programs. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Feb 24]. Available from: http://dx.doi.org/10.26153/tsw/1058.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-2601-7604. Decomposition and variance reduction techniques for stochastic mixed integer programs. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1058

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

4. Pan, Ying-An. Uncertainty quantification for risk assessment of loss-of-coolant accident frequencies in nuclear power plants.

Degree: MSin Engineering, Operations Research and Industrial Engineering, 2013, University of Texas – Austin

 This research presents the methodologies used to resolve the Nuclear Regulatory Commission Generic Safety Issue 191. The presented results are specific to South Texas Project… (more)

Subjects/Keywords: LOCA; Break frequency; Optimization; Simulation; Aggregation; GSI-191; UQ; PRA; CASA; Nuclear

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pan, Y. (2013). Uncertainty quantification for risk assessment of loss-of-coolant accident frequencies in nuclear power plants. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22499

Chicago Manual of Style (16th Edition):

Pan, Ying-An. “Uncertainty quantification for risk assessment of loss-of-coolant accident frequencies in nuclear power plants.” 2013. Masters Thesis, University of Texas – Austin. Accessed February 24, 2020. http://hdl.handle.net/2152/22499.

MLA Handbook (7th Edition):

Pan, Ying-An. “Uncertainty quantification for risk assessment of loss-of-coolant accident frequencies in nuclear power plants.” 2013. Web. 24 Feb 2020.

Vancouver:

Pan Y. Uncertainty quantification for risk assessment of loss-of-coolant accident frequencies in nuclear power plants. [Internet] [Masters thesis]. University of Texas – Austin; 2013. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/2152/22499.

Council of Science Editors:

Pan Y. Uncertainty quantification for risk assessment of loss-of-coolant accident frequencies in nuclear power plants. [Masters Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22499

5. Huang, Hsin-Chan. Stockpiling and resource allocation for influenza preparedness and manufacturing assembly.

Degree: PhD, Operations Research and Industrial Engineering, 2014, University of Texas – Austin

 Stockpiling resources is a pervasive way to handle demand uncertainty and future demand surges. However, stockpiling is subject to costs, including warehousing costs, inventory holding… (more)

Subjects/Keywords: Ventilator stockpiling; Vaccine allocation; Bin delivery effect; Optimization; Influenza preparedness; Manufacturing assembly

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, H. (2014). Stockpiling and resource allocation for influenza preparedness and manufacturing assembly. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/27150

Chicago Manual of Style (16th Edition):

Huang, Hsin-Chan. “Stockpiling and resource allocation for influenza preparedness and manufacturing assembly.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed February 24, 2020. http://hdl.handle.net/2152/27150.

MLA Handbook (7th Edition):

Huang, Hsin-Chan. “Stockpiling and resource allocation for influenza preparedness and manufacturing assembly.” 2014. Web. 24 Feb 2020.

Vancouver:

Huang H. Stockpiling and resource allocation for influenza preparedness and manufacturing assembly. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/2152/27150.

Council of Science Editors:

Huang H. Stockpiling and resource allocation for influenza preparedness and manufacturing assembly. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/27150


University of Texas – Austin

6. Chiralaksanakul, Anukal. Monte Carlo methods for multi-stage stochastic programs.

Degree: PhD, Mechanical Engineering., 2003, University of Texas – Austin

 Stochastic programming is a natural and powerful extension of deterministic mathematical programming, and it is effectively utilized for analyzing optimization problems when the problem’s parameters… (more)

Subjects/Keywords: Monte Carlo method; Stochastic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chiralaksanakul, A. (2003). Monte Carlo methods for multi-stage stochastic programs. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/498

Chicago Manual of Style (16th Edition):

Chiralaksanakul, Anukal. “Monte Carlo methods for multi-stage stochastic programs.” 2003. Doctoral Dissertation, University of Texas – Austin. Accessed February 24, 2020. http://hdl.handle.net/2152/498.

MLA Handbook (7th Edition):

Chiralaksanakul, Anukal. “Monte Carlo methods for multi-stage stochastic programs.” 2003. Web. 24 Feb 2020.

Vancouver:

Chiralaksanakul A. Monte Carlo methods for multi-stage stochastic programs. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2003. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/2152/498.

Council of Science Editors:

Chiralaksanakul A. Monte Carlo methods for multi-stage stochastic programs. [Doctoral Dissertation]. University of Texas – Austin; 2003. Available from: http://hdl.handle.net/2152/498

.