Degree Granting Department
Industrial and Management Systems Engineering
Grisselle Centeno, Ph.D.
Tapas Das, Ph.D.
David Decker, M.D.
Sherwin Kouchekian, Ph.D.
Bo Zeng, Ph.D.
Markov decision processes, chemotherapy, random processes, renewable energy systems, Benders’ decomposition, probabilistic programming
This dissertation focuses on extending solution methods in the area of stochastic optimization. Attention is focused to three specific problems in the field. First, a solution method for mixed integer programs subject to chance constraints is discussed. This class of problems serves as an effective modeling framework for a wide variety of applied problems. Unfortunately, chance constrained mixed integer programs tend to be very challenging to solve. Thus, the aim of this work is to address some of these challenges by exploiting the structure of the deterministic reformulation for the problem. Second, a stochastic program for integrating renewable energy sources into traditional energy systems is developed. As the global push for higher utilization of such green resources increases, such models will prove invaluable to energy system designers. Finally, a process for transforming clinical medical data into a model to assist decision making during the treatment planning phase for palliative chemotherapy is outlined. This work will likely provide decision support tools for oncologists. Moreover, given the new requirements for the usage electronic medical records, such techniques will have applicability to other treatment planning applications in the future.
Scholar Commons Citation
Kuznia, Ludwig Charlemagne, "Extensions of Multistage Stochastic Optimization with Applications in Energy and Healthcare" (2012). USF Tampa Graduate Theses and Dissertations.