Matching Items (6)

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Mathematical Models of Forests: An Exploration of Fire Dynamics in Forests Systems

Description

We analyzed multiple different models that can be utilized when measuring effects effects of fire and fire behavior in a forest ecosystem. In the thesis we focused on exploring ordinary differential equations, stochastic models, and partial differential equations

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Agent

Created

Date Created
  • 2021-05

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A Stochastic Airline Staff Scheduling Model with Risk Considerations that Minimizes Costs

Description

Most staff planning for airline industries are done using point estimates; these do not account for the probabilistic nature of employees not showing up to work, and the airline company

Most staff planning for airline industries are done using point estimates; these do not account for the probabilistic nature of employees not showing up to work, and the airline company risks being under or overstaffed at different times, which increases costs and deteriorates customer service. This model proposes utilizing a stochastic method for American Airlines to schedule their ground crew staff. We developed a stochastic model for scheduling that incorporates the risks of absent employees and as well as reliability so that stakeholders can determine the level of reliability they want to maintain in their system based on the costs. We also incorporated a preferences component to the model in order to increase staff satisfaction in the schedules they get assigned based on their predetermined preferences. Since this is a general staffing model, this can be utilized for an airline crew or virtually any other workforce so long as certain parameters about the population can be determined.

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Agent

Created

Date Created
  • 2016-05

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Stochastic multiscale modeling and statistical characterization of complex polymer matrix composites

Description

There are many applications for polymer matrix composite materials in a variety of different industries, but designing and modeling with these materials remains a challenge due to the intricate architecture

There are many applications for polymer matrix composite materials in a variety of different industries, but designing and modeling with these materials remains a challenge due to the intricate architecture and damage modes. Multiscale modeling techniques of composite structures subjected to complex loadings are needed in order to address the scale-dependent behavior and failure. The rate dependency and nonlinearity of polymer matrix composite materials further complicates the modeling. Additionally, variability in the material constituents plays an important role in the material behavior and damage. The systematic consideration of uncertainties is as important as having the appropriate structural model, especially during model validation where the total error between physical observation and model prediction must be characterized. It is necessary to quantify the effects of uncertainties at every length scale in order to fully understand their impact on the structural response. Material variability may include variations in fiber volume fraction, fiber dimensions, fiber waviness, pure resin pockets, and void distributions. Therefore, a stochastic modeling framework with scale dependent constitutive laws and an appropriate failure theory is required to simulate the behavior and failure of polymer matrix composite structures subjected to complex loadings. Additionally, the variations in environmental conditions for aerospace applications and the effect of these conditions on the polymer matrix composite material need to be considered. The research presented in this dissertation provides the framework for stochastic multiscale modeling of composites and the characterization data needed to determine the effect of different environmental conditions on the material properties. The developed models extend sectional micromechanics techniques by incorporating 3D progressive damage theories and multiscale failure criteria. The mechanical testing of composites under various environmental conditions demonstrates the degrading effect these conditions have on the elastic and failure properties of the material. The methodologies presented in this research represent substantial progress toward understanding the failure and effect of variability for complex polymer matrix composites.

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Agent

Created

Date Created
  • 2016

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Stochastic models of patient access management in healthcare

Description

This dissertation addresses access management problems that occur in both emergency and outpatient clinics with the objective of allocating the available resources to improve performance measures by considering the trade-offs.

This dissertation addresses access management problems that occur in both emergency and outpatient clinics with the objective of allocating the available resources to improve performance measures by considering the trade-offs. Two main settings are considered for estimating patient willingness-to-wait (WtW) behavior for outpatient appointments with statistical analyses of data: allocation of the limited booking horizon to patients of different priorities by using time windows in an outpatient setting considering patient behavior, and allocation of hospital beds to admitted Emergency Department (ED) patients. For each chapter, a different approach based on the problem context is developed and the performance is analyzed by implementing analytical and simulation models. Real hospital data is used in the analyses to provide evidence that the methodologies introduced are beneficial in addressing real life problems, and real improvements can be achievable by using the policies that are suggested.

This dissertation starts with studying an outpatient clinic context to develop an effective resource allocation mechanism that can improve patient access to clinic appointments. I first start with identifying patient behavior in terms of willingness-to-wait to an outpatient appointment. Two statistical models are developed to estimate patient WtW distribution by using data on booked appointments and appointment requests. Several analyses are conducted on simulated data to observe effectiveness and accuracy of the estimations.

Then, this dissertation introduces a time windows based policy that utilizes patient behavior to improve access by using appointment delay as a lever. The policy improves patient access by allocating the available capacity to the patients from different priorities by dividing the booking horizon into time intervals that can be used by each priority group which strategically delay lower priority patients.

Finally, the patient routing between ED and inpatient units to improve the patient access to hospital beds is studied. The strategy that captures the trade-off between patient safety and quality of care is characterized as a threshold type. Through the simulation experiments developed by real data collected from a hospital, the achievable improvement of implementing such a strategy that considers the safety-quality of care trade-off is illustrated.

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Agent

Created

Date Created
  • 2019

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Renewable energy penetration planning for remote power grid

Description

Power generation in remote isolated places is a tough problem. Presently, a common source for remote generation is diesel. However, diesel generation is costly and environmental unfriendly. It is promising

Power generation in remote isolated places is a tough problem. Presently, a common source for remote generation is diesel. However, diesel generation is costly and environmental unfriendly. It is promising to replace the diesel generation with some clean and economical generation sources. The concept of renewable generation offers a solution to remote generation. This thesis focuses on evaluation of renewable generation penetration in the remote isolated grid. A small town named Coober Pedy in South Australia is set as an example. The first task is to build the stochastic models of solar irradiation and wind speed based on the local historical data. With the stochastic models, generation fluctuations and generation planning are further discussed. Fluctuation analysis gives an evaluation of storage unit size and costs. Generation planning aims at finding the relationships between penetration level and costs under constraint of energy sufficiency. The results of this study provide the best penetration level that makes the minimum energy costs. In the case of Coober Pedy, cases of wind and photovoltaic penetrations are studied. The additional renewable sources and suspended diesel generation change the electricity costs. Results show that in remote isolated grid, compared to diesel generation, renewable generation can lower the energy costs.

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Agent

Created

Date Created
  • 2012

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Production scheduling and system configuration for capacitated flow lines with application in the semiconductor backend process

Description

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.

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Agent

Created

Date Created
  • 2011