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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 sales and local deliveries started to see an increase in their demand - making these methods the new way of

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.
ContributorsAguilar, Sarbith Anabella (Author) / Escobedo, Adolfo (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
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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 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

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.
ContributorsKemmer, Ryan Wyeth (Author) / Escobedo, Adolfo (Thesis director) / Maciejewski, Ross (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In the past, Industrial Engineering/Engineering Management Capstone groups have not provided adequate documentation of their project data, results, and conclusions to both the course instructor and their project sponsors. The goal of this project is to mitigate these issues by instituting a knowledge management system with one of ASU’s cloud

In the past, Industrial Engineering/Engineering Management Capstone groups have not provided adequate documentation of their project data, results, and conclusions to both the course instructor and their project sponsors. The goal of this project is to mitigate these issues by instituting a knowledge management system with one of ASU’s cloud storage tools, OSF, and by updating course rubrics to reflect knowledge sharing best practices. This project used existing research to employ tactics that promote the long-term use of this system. In addition, data specialists from ASU Library’s Research and Data Management department were involved.
ContributorsWade, Alexis Nicole (Author) / Juarez, Joseph (Thesis director) / Gel, Esma (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
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Description
The retail cost of video games has remained fairly consistent over the decades as the industry has grown so significantly. Emerging alternatives to buying individual games, such as subscription services, attempt to provide a better deal than the current options. Examining the various attributes that all video games possess, regression

The retail cost of video games has remained fairly consistent over the decades as the industry has grown so significantly. Emerging alternatives to buying individual games, such as subscription services, attempt to provide a better deal than the current options. Examining the various attributes that all video games possess, regression analysis can be performed to look for what factors may impact the retail cost of a game. After performing the analysis, however, the low adjusted R-square values indicate that the analysis only accounts for a small percentage of the retail cost variability. This suggests that the chosen attributes are not reliable in estimating retail cost with a regression analysis.
ContributorsKeller, Caden Matthew (Author) / Michael, Clough (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description

Accessible STEAM (Science, Technology, Engineering, Art, and Mathematics) education is imperative in creating the future innovators of the world. This business proposal is for a K-8 STEAM Museum to be built in the Novus Innovation Corridor on Arizona State University (ASU)’s Tempe campus. The museum will host dynamic spaces that

Accessible STEAM (Science, Technology, Engineering, Art, and Mathematics) education is imperative in creating the future innovators of the world. This business proposal is for a K-8 STEAM Museum to be built in the Novus Innovation Corridor on Arizona State University (ASU)’s Tempe campus. The museum will host dynamic spaces that are constantly growing and evolving as exhibits are built by interdisciplinary capstone student groups- creating an internal capstone project pipeline. The intention of the museum is to create an interactive environment that fosters curiosity and creativity while acting as supplemental learning material to Arizona K-8 curriculum. The space intends to serve the greater Phoenix area community and will cater to underrepresented audiences through the development of accessible education rooted in equality and inclusivity.

ContributorsPeters, Abigail J (Author) / McCarville, Daniel R. (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description

Technology has managed to seamlessly grow into every industry fathomable without much resistance. This could be due to the fact that the majority of industries that have integrated technology have lacked insurmountable barriers which could hold back strategic innovations. Even with a wide array of industries applying technology to their

Technology has managed to seamlessly grow into every industry fathomable without much resistance. This could be due to the fact that the majority of industries that have integrated technology have lacked insurmountable barriers which could hold back strategic innovations. Even with a wide array of industries applying technology to their framework, some haven’t managed to reach the true capability of technological advances. One industry that has both taken wide advantage of technology while also barely scraping the surface of the depth behind its potential has been politics. Electronic voting booths, targeted online marketing campaigns, and live streamed debates have been integral parts of our modern-day political environment, however, approval rating-based forecasting for elections has been an area that isn’t commonly referenced by both large political players.

In an age of information where data can be extracted just about anywhere and interpolated using extensive statistical processing, the fact that systems modeling isn’t a pillar of campaign efforts seems ludicrous. A field that is heavily dependent on pivoting concern based on lack of support would make sense to heavily depend on a modeling system that can accurately predict future points of interest.
This report aims to lay the foundation that can be built upon through providing pitfalls in potential modeling, importance of a modeling system, and a barebones skeleton model in AnyLogic with a scheme of how the model would work. I hope this report can serve political interests by providing context on which modeling can accurately provide insight.

ContributorsSchiazzano, John (Author) / McCarville, Daniel R. (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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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 presented in this paper is developed using publicly available, property specific information sourced from the Zillow and Trulia online real

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.
ContributorsSchuchter, Grant (Author) / Clough, Michael (Thesis director) / Escobedo, Adolfo (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description

Project management is the crucial component for managing and mitigating the inherent risks associated with changes in technology and innovation. The procedures to track the schedule, budget, and scope of various projects in the standard worlds of engineering, manufacturing, construction, etc., are essential elements to the success of the project.

Project management is the crucial component for managing and mitigating the inherent risks associated with changes in technology and innovation. The procedures to track the schedule, budget, and scope of various projects in the standard worlds of engineering, manufacturing, construction, etc., are essential elements to the success of the project. Cost overruns, schedule changes, and other natural risks must be managed effectively. But what happens when a project manager is tasked with delivering an attraction that needs to withstand harsh weather conditions, and millions of people enjoying it every year, for a company with arguably the highest standards for quality and guest satisfaction? This would describe the project managers at Walt Disney Imagineering (WDI) and the projects they oversee have tight budgets, aggressive schedules and require a bit more pixie dust than other engineering projects. However, the universal truth is that no matter the size or the scope of the endeavor, project management processes are absolutely essential to ensuring that every team member can effectively collaborate to deliver the best product.

ContributorsBaker, Molly (Author) / McCarville, Daniel R. (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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Description
The rank aggregation problem has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Generally, rank aggregation is utilized whenever a set of judges (human or non-human) express their preferences over a set of items, and it is necessary to find a consensus ranking that

The rank aggregation problem has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Generally, rank aggregation is utilized whenever a set of judges (human or non-human) express their preferences over a set of items, and it is necessary to find a consensus ranking that best represents these preferences collectively. Many real-world instances of this problem involve a very large number of items, include ties, and/or contain partial information, which brings a challenge to decision-makers. This work makes several contributions to overcoming these challenges. Most attention on this problem has focused on an NP-hard distance-based variant known as Kemeny aggregation, for which solution approaches with provable guarantees that can handle difficult large-scale instances remain elusive. Firstly, this work introduces exact and approximate methodologies inspired by the social choice foundations of the problem, namely the Condorcet criterion, to decompose the problem. To deal with instances where exact partitioning does not yield many subsets, it proposes Approximate Condorcet Partitioning, which is a scalable solution technique capable of handling large-scale instances while providing provable guarantees. Secondly, this work delves into the rank aggregation problem under the generalized Kendall-tau distance, which contains Kemeny aggregation as a special case. This new problem provides a robust and highly-flexible framework for handling ties. First, it derives exact and heuristic solution methods for the generalized problem. Second, it introduces a novel social choice property that encloses existing variations of the Condorcet criterion as special cases. Thirdly, this work focuses on top-k list aggregation. Top-k lists are a special form of item orderings wherein out of n total items only a small number of them, k, are explicitly ordered. Top-k lists are being increasingly utilized in various fields including recommendation systems, information retrieval, and machine learning. This work introduces exact and inexact methods for consolidating a collection of heterogeneous top- lists. Furthermore, the strength of the proposed exact formulations is analyzed from a polyhedral point of view. Finally, this work identifies the top-100 U.S. universities by consolidating four prominent university rankings to assess the computational implications of this problem.
ContributorsAkbari, Sina (Author) / Escobedo, Adolfo (Thesis advisor) / Byeon, Geunyeong (Committee member) / Sefair, Jorge (Committee member) / Wu, Shin-Yi (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Matching or stratification is commonly used in observational studies to remove bias due to confounding variables. Analyzing matched data sets requires specific methods which handle dependency among observations within a stratum. Also, modern studies often include hundreds or thousands of variables. Traditional methods for matched data sets are challenged in

Matching or stratification is commonly used in observational studies to remove bias due to confounding variables. Analyzing matched data sets requires specific methods which handle dependency among observations within a stratum. Also, modern studies often include hundreds or thousands of variables. Traditional methods for matched data sets are challenged in high-dimensional settings, mixed type variables (numerical and categorical), nonlinear andinteraction effects. Furthermore, machine learning research for such structured data is quite limited. This dissertation addresses this important gap and proposes machine learning models for identifying informative variables from high-dimensional matched data sets. The first part of this dissertation proposes a machine learning model to identify informative variables from high-dimensional matched case-control data sets. The outcome of interest in this study design is binary (case or control), and each stratum is assumed to have one unit from each outcome level. The proposed method which is referred to as Matched Forest (MF) is effective for large number of variables and identifying interaction effects. The second part of this dissertation proposes three enhancements of MF algorithm. First, a regularization framework is proposed to improve variable selection performance in excessively high-dimensional settings. Second, a classification method is proposed to classify unlabeled pairs of data. Third, two metrics are proposed to estimate the effects of important variables identified by MF. The third part proposes a machine learning model based on Neural Networks to identify important variables from a more generalized matched case-control data set where each stratum has one unit from case outcome level and more than one unit from control outcome level. This method which is referred to as Matched Neural Network (MNN) performs better than current algorithms to identify variables with interaction effects. Lastly, a generalized machine learning model is proposed to identify informative variables from high-dimensional matched data sets where the outcome has more than two levels. This method outperforms existing algorithms in the literature in identifying variables with complex nonlinear and interaction effects.
ContributorsShomal Zadeh, Nooshin (Author) / Runger, George (Thesis advisor) / Montgomery, Douglas (Committee member) / Shinde, Shilpa (Committee member) / Escobedo, Adolfo (Committee member) / Arizona State University (Publisher)
Created2021