Matching Items (34)
Description
In 2010, for the first time in human history, more than half of the world's total population lived in cities; this number is expected to increase to 60% or more by 2050. The goal of this research effort is to create a comprehensive model and modelling framework for megacities, middleweight cities, and urban agglomerations, collectively referred to as dense urban areas. The motivation for this project comes from the United States Army's desire for readiness in all operating environments including dense urban areas. Though there is valuable insight in research to support Army operational behaviors, megacities are of unique interest to nearly every societal sector imaginable. A novel application for determining both main effects and interactive effects between factors within a dense urban area is a Design of Experiments- providing insight on factor causations. Regression Modelling can also be employed for analysis of dense urban areas, providing wide ranging insights into correlations between factors and their interactions. Past studies involving megacities concern themselves with general trend of cities and their operation. This study is unique in its efforts to model a singular megacity to enable decision support for military operational planning, as well as potential decision support to city planners to increase the sustainability of these dense urban areas and megacities.
ContributorsMathesen, Logan Michael (Author) / Zenzen, Frances (Thesis director) / Jennings, Cheryl (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Performance Based Studies Research Studies Group (PBSRG) at Arizona State University (ASU) has been studying the cause of increased cost and time in construction and other projects for the last 20 years. Through two longitudinal studies with a group of owners in the state of Minnesota (400 tests over six years) and the US Army Medical Command (400 tests over four years), the client/buyer has been identified as the largest risk and source of project cost and time deviations. This has been confirmed by over 1,500 tests conducted over the past 20 years. The focus of this research effort is to analyze the economic and performance impact of a delivery process of construction called the Job Order Contracting (JOC) process, to evaluate the value (in terms of time, cost, and customer satisfaction) achieved when utilizing JOC over other traditional methods to complete projects. JOC's strength is that it minimizes the need for the owner to manage, direct and control (MDC) through a lengthy traditional process of design, bid, and award of a construction contract. The study identifies the potential economic savings of utilizing JOC. This paper looks at the results of an ongoing study surveying eight different public universities. The results of the research show that in comparison to more traditional models, JOC has large cost savings, and is preferable among most owners who have used multiple delivery systems.
ContributorsLi, Hao (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Industrial, Systems (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Zero Waste Management at Arizona State University is an initiative that aims to divert 90% of the waste that goes into the landfills. In order to do this, it is important to focus on the biggest generator of waste every year, which is "Food and Catering". One of the biggest challenges facing the food and catering industry is the lack of efficient and standard processes which results in immense waste every year. As a result, this thesis takes a Lean Six Sigma approach into ASU's zero waste event processes and identifies possible gaps that could be improved. It uses the DMAIC methodology to dive into a standard process for requesting and handling a zero waste event at ASU and concentrates on the logistics behind those zero waste events.
ContributorsShah, Riha Paresh (Author) / McCarville, Daniel R. (Thesis director) / Kellso, James (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Existing machine learning and data mining techniques have difficulty in handling three characteristics of real-world data sets altogether in a computationally efficient way: (1) different data types with both categorical data and numeric data, (2) different variable relations in different value ranges of variables, and (3) unknown variable dependency.This dissertation developed a Partial-Value Association Discovery (PVAD) algorithm to overcome the above drawbacks in existing techniques. It also enables the discovery of partial-value and full-value variable associations showing both effects
of individual variables and interactive effects of multiple variables. The algorithm is compared with Association rule mining and Decision Tree for validation purposes. The results show that the PVAD algorithm can overcome the shortcomings of existing methods.
The second part of this dissertation focuses on knee point detection on noisy data. This extended research topic was inspired during the investigation into categorization for numeric data, which corresponds to Step 1 of the PVAD algorithm. A new mathematical definition of knee point on discrete data is introduced. Due to the unavailability of ground truth data or benchmark data sets, functions used to generate synthetic data are carefully selected and defined. These functions are subsequently employed to create the data sets for this experiment. These synthetic data sets are useful for systematically evaluating and comparing the performance of existing methods. Additionally, a deep-learning model is devised for this problem. Experiments show that the proposed model surpasses existing methods in all synthetic data sets, regardless of whether the samples have single or multiple knee points.
The third section presents the application results of the PVAD algorithm to real-world
data sets in various domains. These include energy consumption data of an Arizona State University (ASU) building, Computer Network, and ASU Engineering Freshmen Retention. The PVAD algorithm is utilized to create an associative network for energy consumption modeling, analyze univariate and multivariate measures of network flow variables, and identify common and uncommon characteristics related to engineering student retention after their first year at the university. The findings indicate that the PVAD algorithm offers the advantage and capability to uncover variable relationships.
ContributorsFok, Ting Yan (Author) / Ye, Nong (Thesis advisor) / Iquebal, Ashif (Committee member) / Ju, Feng (Committee member) / Collofello, James (Committee member) / Arizona State University (Publisher)
Created2023