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Healthcare operations have enjoyed reduced costs, improved patient safety, and
innovation in healthcare policy over a huge variety of applications by tackling prob-
lems via the creation and optimization of descriptive mathematical models to guide
decision-making. Despite these accomplishments, models are stylized representations
of real-world applications, reliant on accurate estimations from historical data to jus-
tify their underlying assumptions. To protect against unreliable estimations which
can adversely affect the decisions generated from applications dependent on fully-
realized models, techniques that are robust against misspecications are utilized while
still making use of incoming data for learning. Hence, new robust techniques are ap-
plied that (1) allow for the decision-maker to express a spectrum of pessimism against
model uncertainties while (2) still utilizing incoming data for learning. Two main ap-
plications are investigated with respect to these goals, the first being a percentile
optimization technique with respect to a multi-class queueing system for application
in hospital Emergency Departments. The second studies the use of robust forecasting
techniques in improving developing countries’ vaccine supply chains via (1) an inno-
vative outside of cold chain policy and (2) a district-managed approach to inventory
control. Both of these research application areas utilize data-driven approaches that
feature learning and pessimism-controlled robustness.
innovation in healthcare policy over a huge variety of applications by tackling prob-
lems via the creation and optimization of descriptive mathematical models to guide
decision-making. Despite these accomplishments, models are stylized representations
of real-world applications, reliant on accurate estimations from historical data to jus-
tify their underlying assumptions. To protect against unreliable estimations which
can adversely affect the decisions generated from applications dependent on fully-
realized models, techniques that are robust against misspecications are utilized while
still making use of incoming data for learning. Hence, new robust techniques are ap-
plied that (1) allow for the decision-maker to express a spectrum of pessimism against
model uncertainties while (2) still utilizing incoming data for learning. Two main ap-
plications are investigated with respect to these goals, the first being a percentile
optimization technique with respect to a multi-class queueing system for application
in hospital Emergency Departments. The second studies the use of robust forecasting
techniques in improving developing countries’ vaccine supply chains via (1) an inno-
vative outside of cold chain policy and (2) a district-managed approach to inventory
control. Both of these research application areas utilize data-driven approaches that
feature learning and pessimism-controlled robustness.
ContributorsBren, Austin (Author) / Saghafian, Soroush (Thesis advisor) / Mirchandani, Pitu (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Distributed Renewable energy generators are now contributing a significant amount of energy into the energy grid. Consequently, reliability adequacy of such energy generators will depend on making accurate forecasts of energy produced by them. Power outputs of Solar PV systems depend on the stochastic variation of environmental factors (solar irradiance, ambient temperature & wind speed) and random mechanical failures/repairs. Monte Carlo Simulation which is typically used to model such problems becomes too computationally intensive leading to simplifying state-space assumptions. Multi-state models for power system reliability offer a higher flexibility in providing a description of system state evolution and an accurate representation of probability. In this study, Universal Generating Functions (UGF) were used to solve such combinatorial problems. 8 grid connected Solar PV systems were analyzed with a combined capacity of about 5MW located in a hot-dry climate (Arizona) and accuracy of 98% was achieved when validated with real-time data. An analytics framework is provided to grid operators and utilities to effectively forecast energy produced by distributed energy assets and in turn, develop strategies for effective Demand Response in times of increased share of renewable distributed energy assets in the grid. Second part of this thesis extends the environmental modelling approach to develop an aging test to be run in conjunction with an accelerated test of Solar PV modules. Accelerated Lifetime Testing procedures in the industry are used to determine the dominant failure modes which the product undergoes in the field, as well as predict the lifetime of the product. UV stressor is one of the ten stressors which a PV module undergoes in the field. UV exposure causes browning of modules leading to drop in Short Circuit Current. This thesis presents an environmental modelling approach for the hot-dry climate and extends it to develop an aging test methodology. This along with the accelerated tests would help achieve the goal of correlating field failures with accelerated tests and obtain acceleration factor. This knowledge would help predict PV module degradation in the field within 30% of the actual value and help in knowing the PV module lifetime accurately.
ContributorsKadloor, Nikhil (Author) / Kuitche, Joseph (Thesis advisor) / Pan, Rong (Thesis advisor) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2017
Description
The main objective of this research is to develop reliability assessment methodologies to quantify the effect of various environmental factors on photovoltaic (PV) module performance degradation. The manufacturers of these photovoltaic modules typically provide a warranty level of about 25 years for 20% power degradation from the initial specified power rating. To quantify the reliability of such PV modules, the Accelerated Life Testing (ALT) plays an important role. But there are several obstacles that needs to be tackled to conduct such experiments, since there has not been enough historical field data available. Even if some time-series performance data of maximum output power (Pmax) is available, it may not be useful to develop failure/degradation mode-specific accelerated tests. This is because, to study the specific failure modes, it is essential to use failure mode-specific performance variable (like short circuit current, open circuit voltage or fill factor) that is directly affected by the failure mode, instead of overall power which would be affected by one or more of the performance variables. Hence, to address several of the above-mentioned issues, this research is divided into three phases. The first phase deals with developing models to study climate specific failure modes using failure mode specific parameters instead of power degradation. The limited field data collected after a long time (say 18-21 years), is utilized to model the degradation rate and the developed model is then calibrated to account for several unknown environmental effects using the available qualification testing data. The second phase discusses the cumulative damage modeling method to quantify the effects of various environmental variables on the overall power production of the photovoltaic module. Mainly, this cumulative degradation modeling approach is used to model the power degradation path and quantify the effects of high frequency multiple environmental input data (like temperature, humidity measured every minute or hour) with very sparse response data (power measurements taken quarterly or annually). The third phase deals with optimal planning and inference framework using Iterative-Accelerated Life Testing (I-ALT) methodology. All the proposed methodologies are demonstrated and validated using appropriate case studies.
ContributorsBala Subramaniyan, Arun (Author) / Pan, Rong (Thesis advisor) / Tamizhmani, Govindasamy (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Wu, Teresa (Committee member) / Kuitche, Joseph (Committee member) / Arizona State University (Publisher)
Created2020