Matching Items (26)

132984-Thumbnail Image.png

Forecasting the 85281 Residential Rental Property

Description

The listing price of residential rental real estate is dependent upon property specific attributes. These attributes involve data that can be tabulated as categorical and continuous predictors. The forecasting model

The listing price of residential rental real estate is dependent upon property specific attributes. These attributes involve data that can be tabulated as categorical and continuous predictors. The forecasting model presented in this paper is developed using publicly available, property specific information sourced from the Zillow and Trulia online real estate databases. The following fifteen predictors were tracked for forty-eight rental listings in the 85281 area code: housing type, square footage, number of baths, number of bedrooms, distance to Arizona State University’s Tempe Campus, crime level of the neighborhood, median age range of the neighborhood population, percentage of the neighborhood population that is married, median year of construction of the neighborhood, percentage of the population commuting longer than thirty minutes, percentage of neighborhood homes occupied by renters, percentage of the population commuting by transit, and the number of restaurants, grocery stores, and nightlife within a one mile radius of the property. Through regression analysis, the significant predictors of the listing price of a rental property in the 85281 area code were discerned. These predictors were used to form a forecasting model. This forecasting model explains 75.5% of the variation in listing prices of residential rental real estate in the 85281 area code.

Contributors

Agent

Created

Date Created
  • 2019-05

134111-Thumbnail Image.png

An optimization model for emergency response crew location within a theme park

Description

Every year, millions of guests visit theme parks internationally. Within that massive population, accidents and emergencies are bound to occur. Choosing the correct location for emergency responders inside of the

Every year, millions of guests visit theme parks internationally. Within that massive population, accidents and emergencies are bound to occur. Choosing the correct location for emergency responders inside of the park could mean the difference between life and death. In an effort to provide the utmost safety for the guests of a park, it is important to make the best decision when selecting the location for emergency response crews. A theme park is different from a regular residential or commercial area because the crowds and shows block certain routes, and they change throughout the day. We propose an optimization model that selects staging locations for emergency medical responders in a theme park to maximize the number of responses that can occur within a pre-specified time. The staging areas are selected from a candidate set of restricted access locations where the responders can store their equipment. Our solution approach considers all routes to access any park location, including areas that are unavailable to a regular guest. Theme parks are a highly dynamic environment. Because special events occurring in the park at certain hours (e.g., parades) might impact the responders' travel times, our model's decisions also include the time dimension in the location and re-location of the responders. Our solution provides the optimal location of the responders for each time partition, including backup responders. When an optimal solution is found, the model is also designed to consider alternate optimal solutions that provide a more balanced workload for the crews.

Contributors

Created

Date Created
  • 2017-12

133986-Thumbnail Image.png

A Strategy for Improved Traffic Flow

Description

Commuting is a significant cost in time and in travel expenses for working individuals and a major contributor to emissions in the United States. This project focuses on increasing the

Commuting is a significant cost in time and in travel expenses for working individuals and a major contributor to emissions in the United States. This project focuses on increasing the efficiency of an intersection through the use of "light metering." Light metering involves a series of lights leading up to an intersection forcing cars to stop further away from the final intersection in smaller queues instead of congregating in a large queue before the final intersection. The simulation software package AnyLogic was used to model a simple two-lane intersection with and without light metering. It was found that light metering almost eliminates start-up delay by preventing a long queue to form in front of the modeled intersection. Shorter queue lengths and reduction in the start-up delays prevents cycle failure and significantly reduces the overall delay for the intersection. However, frequent deceleration and acceleration for a few of the cars occurs before each light meter. This solution significantly reduces the traffic density before the intersection and the overall delay but does not appear to be a better emission alternative due to an increase in acceleration. Further research would need to quantify the difference in emissions for this model compared to a standard intersection.

Contributors

Agent

Created

Date Created
  • 2018-05

130926-Thumbnail Image.png

Metrics to Compare Arc-based and Node-based Districting Models

Description

The outbreak of the coronavirus has impacted retailers and the food industry after they were forced to switch to delivery services due to social distancing measures. During these times, online

The outbreak of the coronavirus has impacted retailers and the food industry after they were forced to switch to delivery services due to social distancing measures. During these times, online sales and local deliveries started to see an increase in their demand - making these methods the new way of staying in business. For this reason, this research seeks to identify strategies that could be implemented by delivery service companies to improve their operations by comparing two types of p-median models (node-based and edge-based). To simulate demand, geographical data will be analyzed for the cities of San Diego and Paris. The usage of districting models will allow the determination on how balance and compact the service regions are within the districts. After analyzing the variability of each demand simulation run, conclusions will be made on whether one model is better than the other.

Contributors

Agent

Created

Date Created
  • 2020-12

131386-Thumbnail Image.png

Input-Elicitation Methods for Crowdsourced Human Computation

Description

Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting

Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.

Contributors

Agent

Created

Date Created
  • 2020-05

Efficient Methodology for Assessing and Improving Secure Shredding Bin Service Sizing

Description

Within recent years, the drive for increased sustainability within large corporations has drastically increased. One critical measure within sustainability is the diversion rate, or the amount of waste diverted from

Within recent years, the drive for increased sustainability within large corporations has drastically increased. One critical measure within sustainability is the diversion rate, or the amount of waste diverted from landfills to recycling, repurposing, or reselling. There are a variety of different ways in which a company can improve their diversion rate, such as repurposing paper. A conventional method would be to simply have a recycling bin for collecting all paper, but the concern for large companies then becomes a security issue as confidential papers may not be safe in a traditional recycling bin. Salt River Project (SRP) has tackled this issue by hiring a third-party vendor (TPV) and having all paper placed into designated, secure shredding bins whose content is shredded upon collection and ultimately recycled into new material. However, while this effort is improving their diversion, the question has arisen of how to make the program viable in the long term based on the costs required to sustain it. To tackle this issue, this thesis will focus on creating a methodology and sampling plan to determine the appropriate level of a third-party recycling service required and to guide efficient bin-sizing solutions. This will in turn allow for SRP to understand how much paper waste is being produced and how accurately they are being charged for TPV services.

Contributors

Agent

Created

Date Created
  • 2020-05

156643-Thumbnail Image.png

The Perception of Graph Properties In Graph Layouts

Description

When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have

When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user’s ability to perceive graph properties for a given graph layout. This study applies previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. A large scale (n = 588) crowdsourced experiment is conducted to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber’s law. Three graph layout algorithms from three representative classes (Force Directed - FD, Circular, and Multi-Dimensional Scaling - MDS) are studied, and the results of this experiment establish the precision of judgment for these graph layouts and properties. The findings demonstrate that the perception of graph density can be modeled with Weber’s law. Furthermore, the perception of the average clustering coefficient can be modeled as an inverse of Weber’s law, and the MDS layout showed a significantly different precision of judgment than the FD layout.

Contributors

Agent

Created

Date Created
  • 2018

156595-Thumbnail Image.png

A Spatial Decision Support System for Oil Spill Response and Recovery

Description

Coastal areas are susceptible to man-made disasters, such as oil spills, which not

only have a dreadful impact on the lives of coastal communities and businesses but also

have lasting and hazardous

Coastal areas are susceptible to man-made disasters, such as oil spills, which not

only have a dreadful impact on the lives of coastal communities and businesses but also

have lasting and hazardous consequences. The United States coastal areas, especially

the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations

that resulted in major economic and ecological losses. These disasters affected the oil,

housing, forestry, tourism, and fishing industries with overall costs exceeding billions

of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been

done with respect to oil spill simulation techniques, spatial optimization models, and

innovative strategies to deal with spill response and planning efforts. However, most

of the research done in those areas is done independently of each other, leaving a

conceptual void between them.

In the following work, this thesis presents a Spatial Decision Support System

(SDSS), which efficiently integrates the independent facets of spill modeling techniques

and spatial optimization to enable officials to investigate and explore the various

options to clean up an offshore oil spill to make a more informed decision. This

thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim

et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill

Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to

spatially optimize the response efforts. The results of this combination are visualized

in the SDSS, featuring geographical maps, so the boat ramps from which the response

should be launched can be easily identified along with the amount of oil that hits the

shore thereby visualizing the intensity of the impact of the spill in the coastal areas

for various cleanup targets.

Contributors

Agent

Created

Date Created
  • 2018

157571-Thumbnail Image.png

Chance-constrained optimization models for agricultural seed development and selection

Description

Breeding seeds to include desirable traits (increased yield, drought/temperature resistance, etc.) is a growing and important method of establishing food security. However, besides breeder intuition, few decision-making tools exist that

Breeding seeds to include desirable traits (increased yield, drought/temperature resistance, etc.) is a growing and important method of establishing food security. However, besides breeder intuition, few decision-making tools exist that can provide the breeders with credible evidence to make decisions on which seeds to progress to further stages of development. This thesis attempts to create a chance-constrained knapsack optimization model, which the breeder can use to make better decisions about seed progression and help reduce the levels of risk in their selections. The model’s objective is to select seed varieties out of a larger pool of varieties and maximize the average yield of the “knapsack” based on meeting some risk criteria. Two models are created for different cases. First is the risk reduction model which seeks to reduce the risk of getting a bad yield but still maximize the total yield. The second model considers the possibility of adverse environmental effects and seeks to mitigate the negative effects it could have on the total yield. In practice, breeders can use these models to better quantify uncertainty in selecting seed varieties

Contributors

Agent

Created

Date Created
  • 2019

157599-Thumbnail Image.png

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.

Contributors

Agent

Created

Date Created
  • 2019