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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
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2011 2020


Bayesian networks are powerful tools in system reliability assessment due to their flexibility in modeling the reliability structure of complex systems. This dissertation develops Bayesian network models for system reliability analysis through the use of Bayesian inference techniques. Bayesian networks generalize fault trees by allowing components and subsystems to be related by conditional probabilities instead of deterministic relationships; thus, they provide analytical advantages to the situation when the failure structure is not well understood, especially during the product design stage. In order to tackle this problem, one needs to utilize auxiliary information such as the reliability information from similar products …

Contributors
Yontay, Petek, Pan, Rong, Montgomery, Douglas C, et al.
Created Date
2016

Network traffic analysis by means of Quality of Service (QoS) is a popular research and development area among researchers for a long time. It is becoming even more relevant recently due to ever increasing use of the Internet and other public and private communication networks. Fast and precise QoS analysis is a vital task in mission-critical communication networks (MCCNs), where providing a certain level of QoS is essential for national security, safety or economic vitality. In this thesis, the details of all aspects of a comprehensive computational framework for QoS analysis in MCCNs are provided. There are three main QoS …

Contributors
Senturk, Muhammet Burhan, Li, Jing, Baydogan, Mustafa G, et al.
Created Date
2014

Complex systems are pervasive in science and engineering. Some examples include complex engineered networks such as the internet, the power grid, and transportation networks. The complexity of such systems arises not just from their size, but also from their structure, operation (including control and management), evolution over time, and that people are involved in their design and operation. Our understanding of such systems is limited because their behaviour cannot be characterized using traditional techniques of modelling and analysis. As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant …

Contributors
Aldaco-Gastelum, Abraham Netzahualcoyotl, Syrotiuk, Violet R., Colbourn, Charles J., et al.
Created Date
2015

Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data imbalance and data noise have been treated separately in the data mining field. Yet, such approach ignores the mutual effects and as a result may lead to new problems. A desirable solution is to tackle these two issues jointly. Noting the complementary nature of generative and discriminative models, this research …

Contributors
He, Miao, Wu, Teresa, Li, Jing, et al.
Created Date
2014

Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount of data collected by traffic sensors. Currently, almost all real-time estimation methods have been developed for estimating laterally aggregated traffic conditions in a roadway segment using link-based models which assume homogeneous conditions across multiple lanes. However, with new advances and applications of ITS, knowledge of lane-based traffic conditions is becoming important, where the traffic condition differences among lanes are recognized. …

Contributors
Zhou, Zhuoyang, Mirchandani, Pitu, Askin, Ronald, et al.
Created Date
2015

A Pairwise Comparison Matrix (PCM) is used to compute for relative priorities of criteria or alternatives and are integral components of widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, a PCM suffers from several issues limiting its application to large-scale decision problems, specifically: (1) to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker (DM), (2) inconsistent and (3) imprecise preferences maybe obtained due to the limited cognitive power of DMs. This dissertation proposes a PCM Framework …

Contributors
Jalao, Eugene Rex Lazaro, Shunk, Dan L, Wu, Teresa, et al.
Created Date
2013

Transfer learning refers to statistical machine learning methods that integrate the knowledge of one domain (source domain) and the data of another domain (target domain) in an appropriate way, in order to develop a model for the target domain that is better than a model using the data of the target domain alone. Transfer learning emerged because classic machine learning, when used to model different domains, has to take on one of two mechanical approaches. That is, it will either assume the data distributions of the different domains to be the same and thereby developing one model that fits all, …

Contributors
Zou, Na, Li, Jing, Baydogan, Mustafa, et al.
Created Date
2015

A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the …

Contributors
Adibi, Azadeh, Montgomery, Douglas, Borror, Connie, et al.
Created Date
2013

In the entire supply chain, demand planning is one of the crucial aspects of the production planning process. If the demand is not estimated accurately, then it causes revenue loss. Past research has shown forecasting can be used to help the demand planning process for production. However, accurate forecasting from historical data is difficult in today's complex volatile market. Also it is not the only factor that influences the demand planning. Factors, namely, Consumer's shifting interest and buying power also influence the future demand. Hence, this research study focuses on Just-In-Time (JIT) philosophy using a pull control strategy implemented with …

Contributors
Sahu, Pranati, Askin, Ronald G., Shunk, Dan L., et al.
Created Date
2012

The main objective of this research is to develop an approach to PV module lifetime prediction. In doing so, the aim is to move from empirical generalizations to a formal predictive science based on data-driven case studies of the crystalline silicon PV systems. The evaluation of PV systems aged 5 to 30 years old that results in systematic predictive capability that is absent today. The warranty period provided by the manufacturers typically range from 20 to 25 years for crystalline silicon modules. The end of lifetime (for example, the time-to-degrade by 20% from rated power) of PV modules is usually …

Contributors
Kuitche, Joseph Mathurin, Pan, Rong, TamizhMani, Govindasamy, et al.
Created Date
2014

In the industry of manufacturing, each gas turbine engine component begins in a raw state such as bar stock and is routed through manufacturing processes to define its final form before being installed on the engine. What is the follow-up to this part? What happens when over time and usage it wears? Several factors have created a section of the manufacturing industry known as aftermarket to support the customer in their need for restoration and repair of their original product. Once a product has reached a wear factor or cycle limit that cannot be ignored, one of the options is …

Contributors
Moedano, Jesus Alberto, Lewis, Sharon L, Meitz, Robert, et al.
Created Date
2013

Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to …

Contributors
Hu, Mengqi, Wu, Teresa, Weir, Jeffery, et al.
Created Date
2012

The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) …

Contributors
Haghnevis, Moeed, Askin, Ronald G, Armbruster, Dieter, et al.
Created Date
2013

Factory production is stochastic in nature with time varying input and output processes that are non-stationary stochastic processes. Hence, the principle quantities of interest are random variables. Typical modeling of such behavior involves numerical simulation and statistical analysis. A deterministic closure model leading to a second order model for the product density and product speed has previously been proposed. The resulting partial differential equations (PDE) are compared to discrete event simulations (DES) that simulate factory production as a time dependent M/M/1 queuing system. Three fundamental scenarios for the time dependent influx are studied: An instant step up/down of the mean …

Contributors
Wienke, Matthew Richard, Armbruster, Dieter, Jones, Donald, et al.
Created Date
2015

The ever-changing economic landscape has forced many companies to re-examine their supply chains. Global resourcing and outsourcing of processes has been a strategy many organizations have adopted to reduce cost and to increase their global footprint. This has, however, resulted in increased process complexity and reduced customer satisfaction. In order to meet and exceed customer expectations, many companies are forced to improve quality and on-time delivery, and have looked towards Lean Six Sigma as an approach to enable process improvement. The Lean Six Sigma literature is rich in deployment strategies; however, there is a general lack of a mathematical approach …

Contributors
Duarte, Brett Marc, Fowler, John W, Montgomery, Douglas C, et al.
Created Date
2011

To guide the timetabling and vehicle assignment of urban bus systems, a group of optimization models were developed for scenarios from simple to complex. The model took the interaction of prospective passengers and bus companies into consideration to achieve the maximum financial benefit as well as social satisfaction. The model was verified by a series of case studies and simulation from which some interesting conclusions were drawn. Dissertation/Thesis Simulation File, including CSV data file

Contributors
Huang, Shiyang, Askin, Ronald G, Mirchandani, Pitu, et al.
Created Date
2014

In recent years, service oriented computing (SOC) has become a widely accepted paradigm for the development of distributed applications such as web services, grid computing and cloud computing systems. In service-based systems (SBS), multiple service requests with specific performance requirements make services compete for system resources. IT service providers need to allocate resources to services so the performance requirements of customers can be satisfied. Workload and performance models are required for efficient resource management and service performance assurance in SBS. This dissertation develops two methods to understand and model the cause-effect relations of service-related activities with resources workload and service …

Contributors
Martinez Aranda, Billibaldo Iram, Ye, Nong, Wu, Tong, et al.
Created Date
2012

No-confounding designs (NC) in 16 runs for 6, 7, and 8 factors are non-regular fractional factorial designs that have been suggested as attractive alternatives to the regular minimum aberration resolution IV designs because they do not completely confound any two-factor interactions with each other. These designs allow for potential estimation of main effects and a few two-factor interactions without the need for follow-up experimentation. Analysis methods for non-regular designs is an area of ongoing research, because standard variable selection techniques such as stepwise regression may not always be the best approach. The current work investigates the use of the Dantzig …

Contributors
Krishnamoorthy, Archana, Montgomery, Douglas C, Borror, Connie, et al.
Created Date
2014

This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and …

Contributors
Lujan Moreno, Gustavo A., Runger, George C, Atkinson, Robert K, et al.
Created Date
2017

Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery dates. Discrete Event Simulation (DES) models are often used to model these complex manufacturing systems in order to generate estimates of the cycle time distribution. However, building models and executing them consumes sufficient time and resources. The objective of this research is to determine the influence of input parameters on …

Contributors
Salvi, Tanushree Ashutosh, Bekki, Jennifer M, Sodemann, Angela, et al.
Created Date
2017

This thesis presents a meta-analysis of lead-free solder reliability. The qualitative analyses of the failure modes of lead- free solder under different stress tests including drop test, bend test, thermal test and vibration test are discussed. The main cause of failure of lead- free solder is fatigue crack, and the speed of propagation of the initial crack could differ from different test conditions and different solder materials. A quantitative analysis about the fatigue behavior of SAC lead-free solder under thermal preconditioning process is conducted. This thesis presents a method of making prediction of failure life of solder alloy by building …

Contributors
Xu, Xinyue, Pan, Rong, Montgomery, Douglas, et al.
Created Date
2014

Optimal experimental design for generalized linear models is often done using a pseudo-Bayesian approach that integrates the design criterion across a prior distribution on the parameter values. This approach ignores the lack of utility of certain models contained in the prior, and a case is demonstrated where the heavy focus on such hopeless models results in a design with poor performance and with wild swings in coverage probabilities for Wald-type confidence intervals. Design construction using a utility-based approach is shown to result in much more stable coverage probabilities in the area of greatest concern. The pseudo-Bayesian approach can be applied …

Contributors
Hassler, Edgar, Montgomery, Douglas C, Silvestrini, Rachel T, et al.
Created Date
2015

A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled …

Contributors
Lee, Dongjin, Pan, Rong, Montgomery, Douglas, et al.
Created Date
2018

This dissertation explores different methodologies for combining two popular design paradigms in the field of computer experiments. Space-filling designs are commonly used in order to ensure that there is good coverage of the design space, but they may not result in good properties when it comes to model fitting. Optimal designs traditionally perform very well in terms of model fitting, particularly when a polynomial is intended, but can result in problematic replication in the case of insignificant factors. By bringing these two design types together, positive properties of each can be retained while mitigating potential weaknesses. Hybrid space-filling designs, generated …

Contributors
Kennedy, Kathryn Sarah, Montgomery, Douglas C, Johnson, Rachel T, et al.
Created Date
2013

Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive …

Contributors
Cui, Can, Wu, Teresa, Weir, Jeffery D., et al.
Created Date
2016

This thesis presents a successful application of operations research techniques in nonprofit distribution system to improve the distribution efficiency and increase customer service quality. It focuses on truck routing problems faced by St. Mary’s Food Bank Distribution Center. This problem is modeled as a capacitated vehicle routing problem to improve the distribution efficiency and is extended to capacitated vehicle routing problem with time windows to increase customer service quality. Several heuristics are applied to solve these vehicle routing problems and tested in well-known benchmark problems. Algorithms are tested by comparing the results with the plan currently used by St. Mary’s …

Contributors
Li, Xiaoyan, Askin, Ronald, Wu, Teresa, et al.
Created Date
2015

Mixture experiments are useful when the interest is in determining how changes in the proportion of an experimental component affects the response. This research focuses on the modeling and design of mixture experiments when the response is categorical namely, binary and ordinal. Data from mixture experiments is characterized by the perfect collinearity of the experimental components, resulting in model matrices that are singular and inestimable under likelihood estimation procedures. To alleviate problems with estimation, this research proposes the reparameterization of two nonlinear models for ordinal data -- the proportional-odds model with a logistic link and the stereotype model. A study …

Contributors
Mancenido, Michelle V., Montgomery, Douglas C, Pan, Rong, et al.
Created Date
2016

Resource allocation in cloud computing determines the allocation of computer and network resources of service providers to service requests of cloud users for meeting the cloud users' service requirements. The efficient and effective resource allocation determines the success of cloud computing. However, it is challenging to satisfy objectives of all service providers and all cloud users in an unpredictable environment with dynamic workload, large shared resources and complex policies to manage them. Many studies propose to use centralized algorithms for achieving optimal solutions for resource allocation. However, the centralized algorithms may encounter the scalability problem to handle a large number …

Contributors
Yang, Su Seon, Ye, Nong, Wu, Teresa, et al.
Created Date
2016

In accelerated life tests (ALTs), complete randomization is hardly achievable because of economic and engineering constraints. Typical experimental protocols such as subsampling or random blocks in ALTs result in a grouped structure, which leads to correlated lifetime observations. In this dissertation, generalized linear mixed model (GLMM) approach is proposed to analyze ALT data and find the optimal ALT design with the consideration of heterogeneous group effects. Two types of ALTs are demonstrated for data analysis. First, constant-stress ALT (CSALT) data with Weibull failure time distribution is modeled by GLMM. The marginal likelihood of observations is approximated by the quadrature rule; …

Contributors
Seo, Kangwon, Pan, Rong, Montgomery, Douglas C, et al.
Created Date
2017

Major advancements in biology and medicine have been realized during recent decades, including massively parallel sequencing, which allows researchers to collect millions or billions of short reads from a DNA or RNA sample. This capability opens the door to a renaissance in personalized medicine if effectively deployed. Three projects that address major and necessary advancements in massively parallel sequencing are included in this dissertation. The first study involves a pair of algorithms to verify patient identity based on single nucleotide polymorphisms (SNPs). In brief, we developed a method that allows de novo construction of sample relationships, e.g., which ones are …

Contributors
Morris, Scott, Gel, Esma S, Runger, George, et al.
Created Date
2014

In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and …

Contributors
Wang, Kun, Li, Jing, Wu, Teresa, et al.
Created Date
2018

This research develops heuristics for scheduling electric power production amid uncertainty. Reliability is becoming more difficult to manage due to growing uncertainty from renewable resources. This challenge is compounded by the risk of resource outages, which can occur any time and without warning. Stochastic optimization is a promising tool but remains computationally intractable for large systems. The models used in industry instead schedule for the forecast and withhold generation reserve for scenario response, but they are blind to how this reserve may be constrained by network congestion. This dissertation investigates more effective heuristics to improve economics and reliability in power …

Contributors
Lyon, Joshua, Zhang, Muhong, Hedman, Kory W, et al.
Created Date
2015

Agricultural supply chains are complex systems which pose significant challenges beyond those of traditional supply chains. These challenges include: long lead times, stochastic yields, short shelf lives and a highly distributed supply base. This complexity makes coordination critical to prevent food waste and other inefficiencies. Yet, supply chains of fresh produce suffer from high levels of food waste; moreover, their high fragmentation places a great economic burden on small and medium sized farms. This research develops planning tools tailored to the production/consolidation level in the supply chain, taking the perspective of an agricultural cooperative—a business model which presents unique coordination …

Contributors
Mason, Andrew Nicholas, Villalobos, Jesus R, Griffin, Paul, et al.
Created Date
2015

Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This dissertation presents several methods for learning and visualizing nonlinear variation in high-dimensional data. First, an automated method for discovering nonlinear variation patterns using deep learning autoencoders is proposed. The approach provides a functional mapping from a low-dimensional representation to the original spatially-dense data that is both interpretable and efficient with respect to preserving information. Experimental results indicate that deep learning autoencoders outperform manifold learning and principal component analysis in reproducing …

Contributors
Howard, Phillip Ryan, Runger, George, Montgomery, Douglas, et al.
Created Date
2016

Every year, more than 11 million maritime containers and 11 million commercial trucks arrive to the United States, carrying all types of imported goods. As it would be costly to inspect every container, only a fraction of them are inspected before being allowed to proceed into the United States. This dissertation proposes a decision support system that aims to allocate the scarce inspection resources at a land POE (L-POE), to minimize the different costs associated with the inspection process, including those associated with delaying the entry of legitimate imports. Given the ubiquity of sensors in all aspects of the supply …

Contributors
Xue, Liangjie, Villalobos, Jesus René, Gel, Esma, et al.
Created Date
2012

Revenue management is at the core of airline operations today; proprietary algorithms and heuristics are used to determine prices and availability of tickets on an almost-continuous basis. While initial developments in revenue management were motivated by industry practice, later developments overcoming fundamental omissions from earlier models show significant improvement, despite their focus on relatively esoteric aspects of the problem, and have limited potential for practical use due to computational requirements. This dissertation attempts to address various modeling and computational issues, introducing realistic choice-based demand revenue management models. In particular, this work introduces two optimization formulations alongside a choice-based demand modeling …

Contributors
Clough, Michael C., Gel, Esma, Jacobs, Timothy, et al.
Created Date
2016

Model-based clustering is a sub-field of statistical modeling and machine learning. The mixture models use the probability to describe the degree of the data point belonging to the cluster, and the probability is updated iteratively during the clustering. While mixture models have demonstrated the superior performance in handling noisy data in many fields, there exist some challenges for high dimensional dataset. It is noted that among a large number of features, some may not indeed contribute to delineate the cluster profiles. The inclusion of these “noisy” features will confuse the model to identify the real structure of the clusters and …

Contributors
Fu, Yinlin, Wu, Teresa, Mirchandani, Pitu, et al.
Created Date
2020

Consumer goods supply chains have gradually incorporated lean manufacturing principles to identify and reduce non-value-added activities. Companies implementing lean practices have experienced improvements in cost, quality, and demand responsiveness. However certain elements of these practices, especially those related to transportation and distribution may have detrimental impact on the environment. This study asks: What impact do current best practices in lean logistics and retailing have on environmental performance? The research hypothesis of this dissertation establishes that lean distribution of durable and consumable goods can result in an increased amount of carbon dioxide emissions, leading to climate change and natural resource depletion …

Contributors
Ugarte, Gustavo Marco, Golden, Jay S., Dooley, Kevin J., et al.
Created Date
2011

Fiber-Wireless (FiWi) network is the future network configuration that uses optical fiber as backbone transmission media and enables wireless network for the end user. Our study focuses on the Dynamic Bandwidth Allocation (DBA) algorithm for EPON upstream transmission. DBA, if designed properly, can dramatically improve the packet transmission delay and overall bandwidth utilization. With new DBA components coming out in research, a comprehensive study of DBA is conducted in this thesis, adding in Double Phase Polling coupled with novel Limited with Share credits Excess distribution method. By conducting a series simulation of DBAs using different components, we found out that …

Contributors
Zhao, Du, Reisslein, Martin, Mcgarry, Michael, et al.
Created Date
2011

This thesis explores the impact of different experimental design strategies for the development of quantile regression based metamodels of computer simulations. In this research, the objective is to compare the resulting predictive accuracy of five experimental design strategies, each of which is used to develop metamodels of a computer simulation of a semiconductor manufacturing facility. The five examined experimental design strategies include two traditional experimental design strategies, sphere packing and I-optimal, along with three hybrid design strategies, which were developed for this research and combine desirable properties from each of the more traditional approaches. The three hybrid design strategies are: …

Contributors
Nimma, Rishikesh Reddy, Bekki, Jennifer M, Lewis, Sharon L, et al.
Created Date
2016

The shift in focus of manufacturing systems to high-mix and low-volume production poses a challenge to both efficient scheduling of manufacturing operations and effective assessment of production capacity. This thesis considers the problem of scheduling a set of jobs that require machine and worker resources to complete their manufacturing operations. Although planners in manufacturing contexts typically focus solely on machines, schedules that only consider machining requirements may be problematic during implementation because machines need skilled workers and cannot run unsupervised. The model used in this research will be beneficial to these environments as planners would be able to determine more …

Contributors
Adams, Katherine Bahia, Sefair, Jorge, Askin, Ronald, et al.
Created Date
2019

In this thesis, a single-level, multi-item capacitated lot sizing problem with setup carryover, setup splitting and backlogging is investigated. This problem is typically used in the tactical and operational planning stage, determining the optimal production quantities and sequencing for all the products in the planning horizon. Although the capacitated lot sizing problems have been investigated with many different features from researchers, the simultaneous consideration of setup carryover and setup splitting is relatively new. This consideration is beneficial to reduce costs and produce feasible production schedule. Setup carryover allows the production setup to be continued between two adjacent periods without incurring …

Contributors
Chen, Cheng-Lung, Zhang, Muhong, Mohan, Srimathy, et al.
Created Date
2015

Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational …

Contributors
YEH, HUAI-MING, Yan, Hao, Pan, Rong, et al.
Created Date
2019

The following is a case study composed of three workflow investigations at the open source software development (OSSD) based Apache Software Foundation (Apache). I start with an examination of the workload inequality within the Apache, particularly with regard to requirements writing. I established that the stronger a participant's experience indicators are, the more likely they are to propose a requirement that is not a defect and the more likely the requirement is eventually implemented. Requirements at Apache are divided into work tickets (tickets). In our second investigation, I reported many insights into the distribution patterns of these tickets. The participants …

Contributors
Panos, Ryan Charles, Collofello, James, Fowler, John, et al.
Created Date
2017

Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering …

Contributors
Bhattacharya, Indrani, Sen, Arunabha, Kempf, Karl G, et al.
Created Date
2013

Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal processing, pattern recognition, data mining, high fidelity probabilistic progressive damage models, physics based damage models, and regression analysis. Due to the wide application of carbon fiber reinforced composites and their multiscale failure mechanisms, it is necessary to emphasize the research of SHM on composite structures. This research develops a comprehensive …

Contributors
Liu, Yingtao, Chattopadhyay, Aditi, Rajadas, John, et al.
Created Date
2012

In this dissertation, an innovative framework for designing a multi-product integrated supply chain network is proposed. Multiple products are shipped from production facilities to retailers through a network of Distribution Centers (DCs). Each retailer has an independent, random demand for multiple products. The particular problem considered in this study also involves mixed-product transshipments between DCs with multiple truck size selection and routing delivery to retailers. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transshipments and routing are involved. In order to find out a good solution effectively, a two-phase solution …

Contributors
Xia, Mingjun, Askin, Ronald, Mirchandani, Pitu, et al.
Created Date
2013

Improving the quality of Origin-Destination (OD) demand estimates increases the effectiveness of design, evaluation and implementation of traffic planning and management systems. The associated bilevel Sensor Location Flow-Estimation problem considers two important research questions: (1) how to compute the best estimates of the flows of interest by using anticipated data from given candidate sensors location; and (2) how to decide on the optimum subset of links where sensors should be located. In this dissertation, a decision framework is developed to optimally locate and obtain high quality OD volume estimates in vehicular traffic networks. The framework includes a traffic assignment model …

Contributors
Wang, Ning, Mirchandani, Pitu, Murray, Alan, et al.
Created Date
2013

Mobile healthy food retailers are a novel alleviation technique to address disparities in access to urban produce stores in food desert communities. Such retailers, which tend to exclusively stock produce items, have become significantly more popular in the past decade, but many are unable to achieve economic sustainability. Therefore, when local and federal grants and scholarships are no longer available for a mobile food retailer, they must stop operating which poses serious health risks to consumers who rely on their services. To address these issues, a framework was established in this dissertation to aid mobile food retailers with reaching economic …

Contributors
Wishon, Christopher John, Villalobos, Rene, Fowler, John, et al.
Created Date
2016

The recent technological advances enable the collection of various complex, heterogeneous and high-dimensional data in biomedical domains. The increasing availability of the high-dimensional biomedical data creates the needs of new machine learning models for effective data analysis and knowledge discovery. This dissertation introduces several unsupervised and supervised methods to help understand the data, discover the patterns and improve the decision making. All the proposed methods can generalize to other industrial fields. The first topic of this dissertation focuses on the data clustering. Data clustering is often the first step for analyzing a dataset without the label information. Clustering high-dimensional data …

Contributors
Lin, Sangdi, Runger, George C, Kocher, Jean-Pierre A, et al.
Created Date
2018