+publisher:"The Ohio State University" +contributor:("Allen, Theodore")

The Ohio State University

1. Lee, Soo Ho. Comparison and Application of Probabilistic Clustering Methods for System Improvement Prioritization.

Degree: PhD, Industrial and Systems Engineering, 2012, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1339766563

► We compare probabilistic clustering methods for analyzing unstructured text or images relevant to prioritizing system improvement actions. Such system improvement activities require an awareness of… (more)

▼ We compare probabilistic clustering methods for analyzing unstructured text or images relevant to prioritizing system improvement actions. Such system improvement activities require an awareness of the entire corpus or set of documents such as transcripts of phone conversations or images. For example, a manager trying to improve the performance of a call center might want to quantitatively understand what the fractions of calls are of a set of types (cluster or topic proportions) and what those types are including the phrases associated phrases (cluster or topic definitions). If a sizable fraction of conversations, e.g., 15%, were using unapproved language, there could be a high priority on implementing standardization or training to reduce cost and improve customer satisfaction related to the identified cluster or topic. We argue that such prioritization could be best understood only if proportions and definitions of all of the clusters or topics can be accounted for accurately.The goal of accurate accounting for the entire corpus is different from information retrieval goals. Information retrieval relates to identifying specific documents of interest in specific queries. As a result, our comparison is based on “ground truth” models of four entire corpora and four measures of distribution fitting accuracy. Yet, the literature on numerical and case study comparisons of probabilistic clustering methods for cases with ground truth standards is lacking. Benefits of comparisons based on ground truth models and given corpora also include the provision of complete examples so that readers can see clearly how different approaches can be applied. Further, using the accuracy of cluster identification permits the comparison of popular methods such as fuzzy clustering together with generative methods such as Bayesian mixture models. This is true as long as we interpret the fuzzy clustering model as a topic model which we do. The resulting “fuzzy topic models” offer demonstrated advantages over latent Dirichlet allocation in repeatability and computational efficiency.These include so-called “topic” models and are generative because they provide a distribution from which entire corpora could be sampled. We provide a numerical study which clarifies the relative accuracy of the probabilistic clustering methods including fuzzy clustering, Principle Component Analysis (PCA) followed by fuzzy clustering, latent Dirichlet allocation (LDA), and the recently proposed Subject Matter Expert Refined Topic (SMERT) Models.We illustrate the application of the methods to the analysis of a call center in the insurance industry. We also illustrate how prioritization-related information can be derived from the corpus with documents. We also provide documentation of how relevant probabilistic clustering methods can be applied. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Industrial Engineering; Subject Matter Expert Refined Topic; SMERT; Comparison; Probabilistic Clustering

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APA (6^th Edition):

Lee, S. H. (2012). Comparison and Application of Probabilistic Clustering Methods for System Improvement Prioritization. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1339766563

Chicago Manual of Style (16^th Edition):

Lee, Soo Ho. “Comparison and Application of Probabilistic Clustering Methods for System Improvement Prioritization.” 2012. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339766563.

MLA Handbook (7^th Edition):

Lee, Soo Ho. “Comparison and Application of Probabilistic Clustering Methods for System Improvement Prioritization.” 2012. Web. 15 Jan 2021.

Vancouver:

Lee SH. Comparison and Application of Probabilistic Clustering Methods for System Improvement Prioritization. [Internet] [Doctoral dissertation]. The Ohio State University; 2012. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1339766563.

Council of Science Editors:

Lee SH. Comparison and Application of Probabilistic Clustering Methods for System Improvement Prioritization. [Doctoral Dissertation]. The Ohio State University; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1339766563

The Ohio State University

2. Afful-Dadzi, Anthony. Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management.

Degree: PhD, Industrial and Systems Engineering, 2012, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1354126687

► Cyber-attacks are considered the greatest domestic security threat in the United States and among the greatest international security threats. Hypothetically, every personal computer connected… (more)

▼ Cyber-attacks are considered the greatest domestic security threat in the United States and among the greatest international security threats. Hypothetically, every personal computer connected to the internet and many other types of devices could be attacked. Many organizations scan all their computers monthly and system administrators attempt to reduce or eliminate vulnerabilities, while juggling other demands on their time. In the first part of this dissertation, we present data from three organizations about both vulnerabilities and remedial actions. We also synthesize sixty-seven articles relating to industrial engineering and operations research (IEOR) and cyber vulnerabilities. We conclude that persistent and critical vulnerabilities result in a large fraction of successful attacks. We then describe the activities and decisions faced by the system administrators and staff members who may be relied on for manual activities that address persistent and critical vulnerabilities. The resulting findings establish an important decision-support role for IEOR contributions to mitigating cyber threat. Also, by analyzing the 67 articles in the Science Citation Index on IEOR topics and cyber vulnerabilities, we are able to identify potential gaps in the existing literature. The second part of the dissertation discusses robust maintenance and monitoring techniques for managing cyber vulnerability. One challenge hindering the effective application of existing models is the scarcity of available data partly because of security concerns. We propose a method based on Markov Decision Processes (MDP) for the generation and graphical evaluation of relevant maintenance policies for cases with limited data availability. The proposed method also provides an estimate of the cost benefit of collecting additional data. Both Bayesian and non-Bayesian formulations of the transition probabilities and cost models of the MDP are considered. We apply the proposed method to a real world cyber vulnerability dataset and generate specific guidance and cost predictions. We also illustrate the relevance of the proposed method to general Markov Decision Process modeling using a numerical example involving three levels of data scarcity. Currently, the number of known cyber vulnerabilities continues to increase exponentially. This complicates the application of control charting which might otherwise be used for monitoring and evaluating the quality level of cyber systems. We describe the challenge and propose residual demerit charts for monitoring quality levels of organizational computer networks. A tangential issue is the comparison of Bayesian and non-Bayesian control charts. Bayesian control charts permit the user to include expert knowledge about a system. However, the fair evaluation of such systems is complicated by the potential mismatch between built-in assumptions (fitting prior), including about the direction of the shift, and method evaluation assumptions (the sampling prior). We end the second part of the… Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Industrial Engineering; Cyber Attack; Value function; Markov Decision Processes; Control Charts

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Afful-Dadzi, A. (2012). Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1354126687

Chicago Manual of Style (16^th Edition):

Afful-Dadzi, Anthony. “Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management.” 2012. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354126687.

MLA Handbook (7^th Edition):

Afful-Dadzi, Anthony. “Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management.” 2012. Web. 15 Jan 2021.

Vancouver:

Afful-Dadzi A. Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management. [Internet] [Doctoral dissertation]. The Ohio State University; 2012. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1354126687.

Council of Science Editors:

Afful-Dadzi A. Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management. [Doctoral Dissertation]. The Ohio State University; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1354126687

The Ohio State University

3. SUI, ZHENHUAN. Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation.

Degree: PhD, Industrial and Systems Engineering, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1499446404436637

► Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an… (more)

▼ Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an unusual vulnerability such as “Heartbleed” that is causing an especially high rate of incidents. This gives rise to the need for timely information to update decision models so that the optimal policies can be generated for each decision period. Social media provides a streaming source of relevant information, but that information needs to be efficiently transformed into numbers to enable the needed updates. This dissertation first explores the use of social media as an observation source for timely decision-making. To efficiently generate the observations for Bayesian updates, the dissertation proposes a novel computational method to fit an existing clustering model, called K-means Latent Dirichlet Allocation (KLDA). The method is illustrated using a cyber security problem related to changing maintenance policies during periods of elevated risk. Also, the dissertation studies four text corpora with 100 replications and show that KLDA is associated with significantly reduced computational times and more consistent model accuracy compared with collapsed Gibbs sampling.Because social media is becoming more popular, researchers have begun applying text analytics models and tools to extract information from these social media platforms. Many of the text analytics models are based on Latent Dirichlet Allocation (LDA). But these models are often poor estimators of topic proportions for emerging topics. Therefore, the second part of dissertation proposes a visual summarizing technique based on topic models, a point system, and Twitter feeds to support passive summarizing and sensemaking. The associated “importance score” point system is intended to mitigate the weakness of topic models. The proposed method is called TWitter Importance Score Topic (TWIST) summarizing method. TWIST employs the topic proportion outputs of tweets and assigns importance points to present trending topics. TWIST generates a chart showing the important and trending topics that are discussed over a given time period. The dissertation illustrates the methodology using two cyber-security field case study examples.Finally, the dissertation proposes a general framework to teach the engineers and practitioners how to work with text data. As an extension of Exploratory Data Analysis (EDA) in quality improvement problems, Exploratory Text Data Analysis (ETDA) implements text as the input data and the goal is to extract useful information from the text inputs for exploration of potential problems and causal effects. This part of the dissertation presents a practical framework for ETDA in the quality improvement projects with four major steps of ETDA: pre-processing text data, text data processing and display, salient feature identification, and salient feature interpretation. For this purpose, various case studies are presented alongside the major steps and tried to discuss these steps with… Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Operations Research; Statistics; Finance; Industrial Engineering; Systems Science; Natural Language Processing, NLP, Machine Learning, Bayesian Statistics, Hierarchical Text Topic Modeling, Text Analytics, Cyber Maintenance, Decision Analysis, Quality Hypothesis Generation, Latent Dirichlet Allocation, Financial Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

SUI, Z. (2017). Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1499446404436637

Chicago Manual of Style (16^th Edition):

SUI, ZHENHUAN. “Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation.” 2017. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1499446404436637.

MLA Handbook (7^th Edition):

SUI, ZHENHUAN. “Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation.” 2017. Web. 15 Jan 2021.

Vancouver:

SUI Z. Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1499446404436637.

Council of Science Editors:

SUI Z. Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1499446404436637

The Ohio State University

4. Xiong, Hui. Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling.

Degree: PhD, Industrial and Systems Engineering, 2011, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048

► Engineers face many quality-related datasets containing free-style text or images. For example, a database could include summaries of complaints filed by customers, or descriptions of… (more)

▼ Engineers face many quality-related datasets containing free-style text or images. For example, a database could include summaries of complaints filed by customers, or descriptions of the causes of rework or maintenance or of the associated actions taken, or a collection of quality inspection images of welded tubes. The goal of this dissertation is to enable engineers to input a database of free-style text or image data and then obtain a set of clusters or “topics” with intuitive definitions and information about the degree of commonality that together helps prioritize system improvement. The proposed methods generate Pareto charts of ranked clusters or topics with their interpretability improved by input from the analyst or method user. The combination of subject matter expert data with standard data is the novel feature of the methods considered. Prior to the methods proposed here, analysts applied Bayesian mixture models and had limited recourse if the cluster or topic definitions failed to be interpretable or are at odds with the knowledge of subject matter experts.The associated “Subject Matter Expert Refined Topic” (SMERT) model permits on-going knowledge elicitation and high-level human expert data integration to address the issues regarding: (1) unsupervised topic models often produce results to user, and (2) to provide a “Hierachical Analysis Designed Latency Experiment” (HANDLE) for human expert to interact with the model results. If grouping are missing key elements, so-called “boosting” these elements is possible. If certain members of a cluster are nonsensical or nonphysical, so-called “zapping” these nonsensical elements is possible. We also describe a fast Collapsed Gibbs Sampling (CGS) algorithm for SMERT method, which offers the capacity to efficiently SMERT model large datasets but which is associated with approximations in certain cases.We use three case studies to illustrate the proposed methods. The first relates to scrap text reports for a Chinese manufacturer of stone products. The second relates to laser welding of tube joints and images characterizing bead shape. The third case study relates to consumer reports text user reviews of the Toyota Camry. The user reviews cover 10 years and the widely publicized acceleration issue. In all cases, the SMERT models help provide interpretable groupings of records in a way that could facilitate data-driven prioritization of improvement actions. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Computer Science; Engineering; Industrial Engineering; Information Technology; quality engineering; Bayesian mixture model; topic model; unstructured data; freestyle text; collapsed Gibbs sampling; text mining; data mining; human computer interaction; subject matter expert

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xiong, H. (2011). Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048

Chicago Manual of Style (16^th Edition):

Xiong, Hui. “Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling.” 2011. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048.

MLA Handbook (7^th Edition):

Xiong, Hui. “Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling.” 2011. Web. 15 Jan 2021.

Vancouver:

Xiong H. Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling. [Internet] [Doctoral dissertation]. The Ohio State University; 2011. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048.

Council of Science Editors:

Xiong H. Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling. [Doctoral Dissertation]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048

The Ohio State University

5. Liu, Enhao. Logistic Regression Model for Predicting Warning “Incident” Rates and Implications for the Common Vulnerability Scoring System.

Degree: MS, Industrial and Systems Engineering, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1494008131762435

► Sophisticated cyber attackers exploit vulnerabilities to access restricted information. It is critical for cyber security administrator to reveal the associations among vulnerabilities and attacks. Since… (more)

Subjects/Keywords: Industrial Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, E. (2017). Logistic Regression Model for Predicting Warning “Incident” Rates and Implications for the Common Vulnerability Scoring System. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1494008131762435

Chicago Manual of Style (16^th Edition):

Liu, Enhao. “Logistic Regression Model for Predicting Warning “Incident” Rates and Implications for the Common Vulnerability Scoring System.” 2017. Masters Thesis, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494008131762435.

MLA Handbook (7^th Edition):

Liu, Enhao. “Logistic Regression Model for Predicting Warning “Incident” Rates and Implications for the Common Vulnerability Scoring System.” 2017. Web. 15 Jan 2021.

Vancouver:

Liu E. Logistic Regression Model for Predicting Warning “Incident” Rates and Implications for the Common Vulnerability Scoring System. [Internet] [Masters thesis]. The Ohio State University; 2017. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1494008131762435.

Council of Science Editors:

Liu E. Logistic Regression Model for Predicting Warning “Incident” Rates and Implications for the Common Vulnerability Scoring System. [Masters Thesis]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1494008131762435

The Ohio State University

6. Jiang, Tianyu. Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes.

Degree: MS, Industrial and Systems Engineering, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845

► Cyber vulnerability can be exploited by cyber-attackers to achieve valuable information, alter or destroy a cyber-target. Finding a way to generate appropriate cyber vulnerability maintenance… (more)

Subjects/Keywords: Operations Research; Cyber attackers, Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities; Markov Decision Processes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Jiang, T. (2017). Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845

Chicago Manual of Style (16^th Edition):

Jiang, Tianyu. “Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes.” 2017. Masters Thesis, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845.

MLA Handbook (7^th Edition):

Jiang, Tianyu. “Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes.” 2017. Web. 15 Jan 2021.

Vancouver:

Jiang T. Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes. [Internet] [Masters thesis]. The Ohio State University; 2017. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845.

Council of Science Editors:

Jiang T. Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes. [Masters Thesis]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845

The Ohio State University

7. Huang, Shijie. Waiting Lines and System Selection in Constrained Service Systems with Applications in Election Resource Allocation.

Degree: PhD, Industrial and Systems Engineering, 2016, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1471541297

► In the United States, voting is one of the most fundamental rights protected by the federal law. However, there is a growing concern over voter… (more)

▼ In the United States, voting is one of the most fundamental rights protected by the federal law. However, there is a growing concern over voter disenfranchisement and system inequity caused by long waiting lines. Studies have found that inappropriate voting resource allocation has deterred hundreds of thousands of U.S. citizens from voting. This research proposes an analytic framework to quantify the number of deterred voters and assess the resulting political consequences. We apply this framework to the 2012 Sandoval County General Election in New Mexico to study whether electoral outcomes were altered by “effective disenfranchisement” for a few closely contested offices. Furthermore, Indifference-Zone Generalized Binary Search (IZGBS) is developed to determine the minimum sets of resources needed to satisfy a given service level for elections with one or multiple bottleneck resources. What makes the problem challenging is the service level generally needs to be evaluated using discrete event simulations, which is slow and noisy. For example, there are no analytical methods to estimate the waiting time of the 99th percentile voter. Thus, simulation-based ranking and selection (R&S), also known as design and analysis of R&S experiments, are preferred than stochastic integer programming methods in this type of problems. The proposed approach incorporates constrained R&S with Generalized Binary Search (GBS) to provide computationally efficient and rigorous solutions. It also explores the intuitive monotone assumption that adding resources cannot harm average performance for relevant Discrete Event Simulation (DES) models, which has received little attention in the simulation literature previously.The method is based on the assumption of normal populations (but not requiring equal variances). Therefore, special attention is given to quantile-related outputs that are associated with asymptotically normal estimates. The asymptote is in the number of simulated voters on Election Day, which is often over one thousand. Later, IZGBS is applied to the Franklin County of Ohio and Sandoval County of New Mexico to establish the resource requirement so that voters can expect to wait less than thirty minutes. Several topics are suggested for future research, including building on IZGBS to provide optimization solutions for more general problems. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Industrial Engineering; Operations Research; Multiple Comparisons; Comparison with a Standard; Ranking and Selection; Indifference-zone; Generalized Binary Search; Simulation-based Optimization; Quantile Estimator; Election; Voting System; Equity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Huang, S. (2016). Waiting Lines and System Selection in Constrained Service Systems with Applications in Election Resource Allocation. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1471541297

Chicago Manual of Style (16^th Edition):

Huang, Shijie. “Waiting Lines and System Selection in Constrained Service Systems with Applications in Election Resource Allocation.” 2016. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471541297.

MLA Handbook (7^th Edition):

Huang, Shijie. “Waiting Lines and System Selection in Constrained Service Systems with Applications in Election Resource Allocation.” 2016. Web. 15 Jan 2021.

Vancouver:

Huang S. Waiting Lines and System Selection in Constrained Service Systems with Applications in Election Resource Allocation. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1471541297.

Council of Science Editors:

Huang S. Waiting Lines and System Selection in Constrained Service Systems with Applications in Election Resource Allocation. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1471541297

8. Hou, Chengjun. Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management.

Degree: PhD, Industrial and Systems Engineering, 2015, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437676379

► The trustworthiness of models and optimization is limited because the associated systems might be changing and data about them can be limited, i.e., there is… (more)

▼ The trustworthiness of models and optimization is limited because the associated systems might be changing and data about them can be limited, i.e., there is "parametric" uncertainty. This dissertation provides applications and theory related to mitigating the effects of changing systems and data limitations in optimal decision-making.The primary application considered relates to reducing the maintenance costs associated with cyber security. By selecting optimal policies addressing data limitations, losses from stolen information and maintenance costs can be balanced. The approximated expected savings from implementing the suggested policies at a large Midwestern organization is over $14M with a discount factor of 0.95 monthly.The dissertation also integrates data and dynamic programming models for project management decision-making that accounts for coordination and planning costs. This facilitates more accurate schedules with significant cost savings. Insights are provided into the choice between traditional planning methods and agile project management methods that reduce planning complexity. In many situations, we find that the so-called optimal approaches are suboptimal because they fail to address sizable coordination and planning costs.Two types of parametric uncertainty are explored here, each of which results in fundamentally different formulations and solution schemes. The first type of uncertainty considered relates to system parameters fluctuating over time randomly. The related models differ from ordinary inhomogeneous approaches because the specific parameters are not known and are assumed to fluctuate with known distributions. Associated decision problems are referred to as "Markov decision processes with random inhomogeneity" and proposed optimal solutions methods. Proof is given that the solution produced by backward induction is optimal for the finite horizon problems, and that the value-iteration-based algorithm gives solutions converging to the infinite horizon solutions, together with results regarding monotonicity property and rate of the convergence.The second type of parametric uncertainty is caused by insufficient data for parameter estimation, i.e., "data-driven" uncertainty. Previous researchers studying data-driven Markov decision processes declare the problem is intractable. Therefore, they propose approximation methods. We prove that their methods can approximate suboptimal solutions by a numerical example. We also provide a dynamic programming algorithm to generate data-driven optimal policies with learning. We do this by demonstrating that the problem is equivalent to partially observable Markov decision processes. Further, by exploiting the structure of the problem and bounds assuming perfect information, we develop a bounding heuristic method for the infinite horizon problems. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Industrial Engineering; Operations Research; Cyber security; Dynamic programming; Markov decision processes; Parametric uncertainty; Project management

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APA (6^th Edition):

Hou, C. (2015). Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1437676379

Chicago Manual of Style (16^th Edition):

Hou, Chengjun. “Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management.” 2015. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437676379.

MLA Handbook (7^th Edition):

Hou, Chengjun. “Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management.” 2015. Web. 15 Jan 2021.

Vancouver:

Hou C. Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437676379.

Council of Science Editors:

Hou C. Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1437676379

9. Xie, Chen. DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES.

Degree: PhD, Industrial and Systems Engineering, 2014, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1398991609

► This dissertation concerns model uncertainty in the context of Markov Decision Processes (MDPs). We argue that, taking data limitations into account, the system is no… (more)

▼ This dissertation concerns model uncertainty in the context of Markov Decision Processes (MDPs). We argue that, taking data limitations into account, the system is no longer Markovian since the transition probabilities are not known. Also, the amount of available data from history can be viewed as part of the system state description. This motivates our introduction of ``Dynamic Decision Approximate Empirical Reward" (DDAER) as a generalization of MDPs. We offer a straightforward expected reward formulation for finite horizon problems and solve it with a generalized backward induction algorithm and prove its convergence together with solution quality guarantees. \par For infinite horizon problems, we prove that generalized contraction mapping is not guaranteed to converge using a counterexample. For small problems, we establish that (assuming sample normality) enumeration combined with ranking and selection methods offers solution quality guarantees. Further, we propose a formulation involving a new type of probabilistic constraint which is the probability that the policy would be found to be optimal with perfect information. We show that a so-called ``two phase" method combining simultaneous multinomial intervals in phase I and selection and ranking in phase II is associated with guarantees of solution quality. \parSeveral numerical examples illustrate the application of the proposed methods. In relation to numerical test problems, we provide a comparison table to illustrate the potentially important practical benefits of the proposed infinite horizon methods compared with alternatives including robust and Bayesian adaptive methods. The benefits derive from the fact that robust methods are conservative and the naive methods fail to account for the amount of available data. Next, we formulate jobshop scheduling as a reward process. Then, we demonstrate the application of the proposed ``two-phase" algorithm. The solutions provide insights into the advisability of shortest processing time heuristics for situations in which many jobs are late and earliest due date heuristic for cases in which few jobs are known to be late.\parWe also offer suggestions for future research. These include addressing larger problem sizes with more states and actions using extensions of the proposed methods. Also, we conjecture the possibility of a solution for the infinite horizon problem using a scenario set approach as was done for finite horizon problems. Further, by viewing the proposed methods as a ``big data" analysis tool, desirable aggregations of states or actions, having fewer states and actions could offer benefits in terms of improved solutions and computational speed. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Engineering; Industrial Engineering; Markov Decision Processes, Parametric Uncertainty, Approximate Decision, Dynamic Reward Processes

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APA (6^th Edition):

Xie, C. (2014). DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1398991609

Chicago Manual of Style (16^th Edition):

Xie, Chen. “DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES.” 2014. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398991609.

MLA Handbook (7^th Edition):

Xie, Chen. “DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES.” 2014. Web. 15 Jan 2021.

Vancouver:

Xie C. DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1398991609.

Council of Science Editors:

Xie C. DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1398991609

10. Roychowdhury, Sayak. Investigation of Flash-free Die Casting by Overflow Design Optimization.

Degree: MS, Industrial and Systems Engineering, 2014, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406121850

► In die casting process, flash is a common problem caused by the impact pressure spike of the molten metal inside the die cavity. This can… (more)

Subjects/Keywords: Industrial Engineering; Operations Research; Die Casting, Flash, Overflow, Hydraulic Bench Test, Optimization, Mathematical Model

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APA (6^th Edition):

Roychowdhury, S. (2014). Investigation of Flash-free Die Casting by Overflow Design Optimization. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1406121850

Chicago Manual of Style (16^th Edition):

Roychowdhury, Sayak. “Investigation of Flash-free Die Casting by Overflow Design Optimization.” 2014. Masters Thesis, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406121850.

MLA Handbook (7^th Edition):

Roychowdhury, Sayak. “Investigation of Flash-free Die Casting by Overflow Design Optimization.” 2014. Web. 15 Jan 2021.

Vancouver:

Roychowdhury S. Investigation of Flash-free Die Casting by Overflow Design Optimization. [Internet] [Masters thesis]. The Ohio State University; 2014. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406121850.

Council of Science Editors:

Roychowdhury S. Investigation of Flash-free Die Casting by Overflow Design Optimization. [Masters Thesis]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406121850

11. Roychowdhury, Sayak. Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance.

Degree: PhD, Industrial and Systems Engineering, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091

► This research explores deterministic and stochastic policies to help organizations make data-driven optimal decisions. The two major application areas identified in this research are manufacturing… (more)

▼ This research explores deterministic and stochastic policies to help organizations make data-driven optimal decisions. The two major application areas identified in this research are manufacturing and cyber security. In a recent report published by McKinsey Analytics, the manufacturing industry uses only 20%-30% of the potential of data analytics. This suggests that there are still plenty of opportunities to use analytics in manufacturing processes. In the first part of my research, I formulate an Integer Programming model for the “stamping” process in automotive manufacturing. I develop a production scheduling method for automotive stamping to maintain optimal inventory positions. In stamping, different types of parts are scheduled for processing in the press, which requires different die-sets to be mounted on the press. This has all the elements of conventional scheduling problems with tardiness objectives and setup costs. Yet, it also has capacity constraints and part production constraints. We show that these constraints make solution with branch and bound difficult for problem sizes of interest. In this research, I use the structure of the scheduling problem and implemented heuristic methods like Genetic Algorithm alongside Earliest Due-date (EDD) rules to prioritize production of parts with low inventory as well as minimize the number of die-set changeovers. I call this new method Genetic Algorithm with Generalized Earliest Due-date (GAGEDD). I illustrate the computational advantages compared with alternatives and show its benefits using data from a real life automotive stamping press scheduling problem to build a decision support tool for the schedulers.The second part of this research is motivated towards improving cyber vulnerability maintenance policies under uncertainty. A conservative estimate by McAfee in 2014 puts annual cost of cybercrime at US$375B. This is an important contemporary issue where role of data analytics and optimization have a lot to offer. Here I implement stochastic optimization procedures for cybersecurity applications, where learning is incorporated to account for future rewards. First, I formulate a Partially Observable Markov Decision Process (POMDP) model to derive policies for cases when the state of compromise of a host is uncertain. This method assumes there is no parametric uncertainty. Next, I implement Bayes Adaptive Markov Decision Process model (BAMDP) on a dataset obtained from the cyber logs of an organization using finite numbers of model scenarios. Earlier BAMDP formulations use infinite model scenarios. I also describe the benefits of using finite scenarios including the ability to solve the problem optimally as a POMDP. The resulting BAMDP formulation accounts for the parametric uncertainty caused by the lack of data for certain events. I use a point based value iteration method known as PERSEUS to solve both of these problems to generate a-vectors, that can be used to design optimal policies based on the belief-state of the system. Another benefit of using finite… Advisors/Committee Members: Allen, Theodore T. (Advisor).

Subjects/Keywords: Industrial Engineering; Operations Research; Operations Research; Scheduling; Automotive Manufacturing; Stamping; Genetic Algorithm; Partially Observable Markov Decision Process; Bayesian Adaptive Markov Decision Process; Cyber security; Cyber vulnerability maintenance

…M.S. (ISE), The Ohio State University, Columbus, Ohio USA 2016… …By Overflow Design. (Masters thesis, The Ohio State University). Roychowdhury, S… …Graduate Teaching Associate, Department of Integrated System Engineering, The Ohio State… …University, Columbus, Ohio USA 2014…

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APA (6^th Edition):

Roychowdhury, S. (2017). Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091

Chicago Manual of Style (16^th Edition):

Roychowdhury, Sayak. “Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance.” 2017. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091.

MLA Handbook (7^th Edition):

Roychowdhury, Sayak. “Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance.” 2017. Web. 15 Jan 2021.

Vancouver:

Roychowdhury S. Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091.

Council of Science Editors:

Roychowdhury S. Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091

12. Zugelder, Thomas J. Lean Six Sigma Literature: A Review and Agenda for Future Research.

Degree: MS, Industrial and Systems Engineering, 2012, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1331160007

► The purpose of this thesis is to investigate the trends in published research concerning Lean Six Sigma and continuous improvement. We use published research in… (more)

Subjects/Keywords: Industrial Engineering; Lean Six Sigma; Capability Maturity Model; Sustainability

…Ohio State University, University of Bath, etc. Manufacturing (M), Service (S… …Conference 16 Levels Industrial (I), Academic (A), or Both (IA) The…

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APA (6^th Edition):

Zugelder, T. J. (2012). Lean Six Sigma Literature: A Review and Agenda for Future Research. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1331160007

Chicago Manual of Style (16^th Edition):

Zugelder, Thomas J. “Lean Six Sigma Literature: A Review and Agenda for Future Research.” 2012. Masters Thesis, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1331160007.

MLA Handbook (7^th Edition):

Zugelder, Thomas J. “Lean Six Sigma Literature: A Review and Agenda for Future Research.” 2012. Web. 15 Jan 2021.

Vancouver:

Zugelder TJ. Lean Six Sigma Literature: A Review and Agenda for Future Research. [Internet] [Masters thesis]. The Ohio State University; 2012. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1331160007.

Council of Science Editors:

Zugelder TJ. Lean Six Sigma Literature: A Review and Agenda for Future Research. [Masters Thesis]. The Ohio State University; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1331160007

13. Hari, Rohit. KNOWLEDGE DISCOVERY USING DATA ANALYSIS TECHNIQUES AND INVERSE EXTREME VALUE STATISTICS TO BETTER PREDICT LIFE OF A BEARING.

Degree: MS, Industrial and Systems Engineering, 2013, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366304299

► Predicting bearing life is an important task for bearing companies because it is vital in the eyes of the customer. Testing methods have improved a… (more)

▼ Predicting bearing life is an important task for bearing companies because it is vital in the eyes of the customer. Testing methods have improved a lot with the advancements in technology. Predicting the bearing life accurately and creating models which provide results very close to the actual measured values provided by these advanced methods thus becomes a difficult and important challenge for many manufacturing companies. Here, we focus on cases in which there is an ability to collect special types of data. In particular, data can be collected about the inclusion size and tested life from a large number of samples having the same material, from the same plant, of the same design, and from the same process. Further, with respect to predicting the future life of a given bearing, there is an ability to measure the maximum inclusion size from a sample of material from the same batch of material. Under all these conditions, the thesis proposes a highly accurate estimation procedure. A model is created using concepts from literature, which discuss about the inclusions and their effect on bearing life, Rolling Contact Fatigue (RCF) mechanisms and Extreme value statistics.The proposed procedure involves the following steps -1.Filter the data based on the parameters i.e. same plant, process, design and material quality 2.Fit an extreme value distribution to the set of data collected on life and compute of the distribution parameters.3.Using these parameters and performing some transformations a plot is constructed. Now this model represents bearings population which have the same set of parameters as mentioned in step 14.Data on inclusions is collected from the materials that are used in the manufacturing of the remainder of the bearings which have the same set of parameters.5.Using the plot we can compute the life of the bearing. By using the method described above a model was created which accurately predicts the life. Upon carrying out a verification process with new data sets showed that the new model is able to predict life values which are much closer to the measured values, when compared to the current model being used. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Industrial Engineering

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APA (6^th Edition):

Hari, R. (2013). KNOWLEDGE DISCOVERY USING DATA ANALYSIS TECHNIQUES AND INVERSE EXTREME VALUE STATISTICS TO BETTER PREDICT LIFE OF A BEARING. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1366304299

Chicago Manual of Style (16^th Edition):

Hari, Rohit. “KNOWLEDGE DISCOVERY USING DATA ANALYSIS TECHNIQUES AND INVERSE EXTREME VALUE STATISTICS TO BETTER PREDICT LIFE OF A BEARING.” 2013. Masters Thesis, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366304299.

MLA Handbook (7^th Edition):

Hari, Rohit. “KNOWLEDGE DISCOVERY USING DATA ANALYSIS TECHNIQUES AND INVERSE EXTREME VALUE STATISTICS TO BETTER PREDICT LIFE OF A BEARING.” 2013. Web. 15 Jan 2021.

Vancouver:

Hari R. KNOWLEDGE DISCOVERY USING DATA ANALYSIS TECHNIQUES AND INVERSE EXTREME VALUE STATISTICS TO BETTER PREDICT LIFE OF A BEARING. [Internet] [Masters thesis]. The Ohio State University; 2013. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366304299.

Council of Science Editors:

Hari R. KNOWLEDGE DISCOVERY USING DATA ANALYSIS TECHNIQUES AND INVERSE EXTREME VALUE STATISTICS TO BETTER PREDICT LIFE OF A BEARING. [Masters Thesis]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366304299

14. Raqab, Alah. GAINING MONITORING CAPABILITIES AND INSIGHTS INTO RESPONSES FROM PHISHING DATA.

Degree: MS, Industrial and Systems Engineering, 2014, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1397504041

► Cyber-attacks are considered the greatest domestic security threat in the United States and among the greatest international security threats. In the recent past, phishing and… (more)

▼ Cyber-attacks are considered the greatest domestic security threat in the United States and among the greatest international security threats. In the recent past, phishing and “denial of service” attacks are starting to become the most relevant forms of cyber intrusion, even while they can involve exploiting system vulnerabilities. Specifically, phishing attacks are reaching the level at which many large organizations are seriously considering purchasing technology and adopting mitigating practices. Therefore, data-driven decision support technology relating to mitigating or avoiding phishing and denial of service attacks are increasingly relevant. A key element of the proposed approach is to treat management of phishing and denial of service cyber-attacks in a manner similar to quality management in production systems. Phishing control charting can become critical tools in both moving target (MT) decision-making and metric development, just as similar techniques are already in manufacturing and service operations. In this thesis, we explore the case study application of design for six sigma to create a proposed integrated system response to phishing email attacks. Specifically, we used a CTQ flow diagram to clarify the relevance of CTQ characteristics including the number of phishing emails and the number of suspended accounts. In Chapter 3, we describe the observed autocorrelations in time series corresponding to both CTQ characteristics. This motivated the use of moving centerline demerit (MCD) charts from a standard reference. From developing an interrelationship diagram, we identified several important interrelationships including the relationship between phishing emails and organizational password policies. Clear seasonality was observed in the data suggesting that responsiveness in certain months (January and summer months) are months are more critical than other months. Strong patterns were identified in that selected sub-populations were much more prone to being tricked by the emails and giving away their information. We omit the specific populations and organizational details for security reasons but the Pareto 80-20 rule was observed to be highly relevant in formulating system responses. We developed a simple charting method based on word frequencies to provide recent information summarizing the nature of phishing attacks. By integrating previous conclusions, we formulated a recommended system response that targets sub-populations, uses the proposed text series charts, and actually reduces password changing requirements on several sub-populations. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Industrial Engineering; Statistics; Operations Research; Cyber-attacks; phishing; design for six sigma; quality contorl

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APA (6^th Edition):

Raqab, A. (2014). GAINING MONITORING CAPABILITIES AND INSIGHTS INTO RESPONSES FROM PHISHING DATA. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1397504041

Chicago Manual of Style (16^th Edition):

Raqab, Alah. “GAINING MONITORING CAPABILITIES AND INSIGHTS INTO RESPONSES FROM PHISHING DATA.” 2014. Masters Thesis, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397504041.

MLA Handbook (7^th Edition):

Raqab, Alah. “GAINING MONITORING CAPABILITIES AND INSIGHTS INTO RESPONSES FROM PHISHING DATA.” 2014. Web. 15 Jan 2021.

Vancouver:

Raqab A. GAINING MONITORING CAPABILITIES AND INSIGHTS INTO RESPONSES FROM PHISHING DATA. [Internet] [Masters thesis]. The Ohio State University; 2014. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1397504041.

Council of Science Editors:

Raqab A. GAINING MONITORING CAPABILITIES AND INSIGHTS INTO RESPONSES FROM PHISHING DATA. [Masters Thesis]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1397504041

The Ohio State University

15. Ittiwattana, Waraporn. A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations.

Degree: PhD, Industrial and Systems Engineering, 2002, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1030366020

► This dissertation contains the first proof of convergence of a genetic algorithm in the context of stochastic optimization. The class of stochastic optimization problems… (more)

▼ This dissertation contains the first proof of convergence of a genetic algorithm in the context of stochastic optimization. The class of stochastic optimization problems includes formulations in which the objective is an expected value, which can be evaluated using Monte Carlo methods. Growing computer power combined with methods presented here and elsewhere makes feasible the solution of many stochastic optimization problems with applications ranging from process design to facility location. The dissertation also describes the proposed stochastic optimization method that combines a sequential ranking and selection procedure with an elitist genetic algorithm. A batching procedure is included to assure that batch means of solutions achieve approximate normality. The proposed method is proven under the normality assumption to converge in the long run to identify and maintain solutions with objective values within an acceptable difference, D, from the global optimal solution with probability greater than an acceptable probability, <i>P*</i>. Computational results illustrate that the proposed algorithm achieves promising performance compared with alternatives for a variety of problems with minimal changes. The first application is on the stochastic optimization for “robust” engineering process design decisions making. By robust we mean designs that maximize the expected utility taking into account variation of “noise factors”. A methodology for robust process design is presented based on direct minimization of the expected loss in some cases using the proposed optimization heuristics. The proposed methods are compared with alternatives including methods based on Taguchi’s signal-to-noise ratios. Several formulations of the loss are explored. The method is illustrated through its application to the design of robotic gas metal arc-welding parameter settings. The second application is a simulation optimization method applied to decision making about where to locate facilities and how to transport products in a supply chain. This problem is shown to be a stochastic generalized assignment problem for which a bound is presented. We also propose a genetic algorithm, for cases in which bounds are available, that offers the possibility of stopping while guaranteeing that a solution with objective value within an acceptable difference, Δ, of the optimal value is found with probability greater than <i>P*</i>. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Genetic Algorithms; Stochastic Optimization; Monte Carlo Methods; Time Non-homogenous Markov Process; Taguchi Methods; Signal-to-Noise Ratio; Parameter Design; Multicriterion Optimization; Global Supply Chain Modeling and Optimization; Facility Location

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ittiwattana, W. (2002). A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1030366020

Chicago Manual of Style (16^th Edition):

Ittiwattana, Waraporn. “A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations.” 2002. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1030366020.

MLA Handbook (7^th Edition):

Ittiwattana, Waraporn. “A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations.” 2002. Web. 15 Jan 2021.

Vancouver:

Ittiwattana W. A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations. [Internet] [Doctoral dissertation]. The Ohio State University; 2002. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1030366020.

Council of Science Editors:

Ittiwattana W. A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations. [Doctoral Dissertation]. The Ohio State University; 2002. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1030366020

The Ohio State University

16. Chantarat, Navara. Modern design of experiments methods for screening and experimentations with mixture and qualitative variables.

Degree: PhD, Industrial and Systems Engineering, 2003, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056

► This dissertation re-examines some of the most basic design of experiment methods with respect to their ability to achieve intuitive objectives. For example, it provides… (more)

▼ This dissertation re-examines some of the most basic design of experiment methods with respect to their ability to achieve intuitive objectives. For example, it provides probably the first comprehensive evaluation of the ability of standard screening approaches to correctly tell which factors have important effects on average outputs. Also, the dissertation examines the prediction errors that users of so-called mixture experimental design and qualitative response surface methods can achieve. In practical situations, the derived "decision support" information can tell the user in advance whether the number of runs used is adequate for the experimenter's needs and provide a basis for selecting one method over another when alternatives are presented. Also, the dissertation clarifies, perhaps for the first time, the potentially serious prediction error issues associated with the methods that have been proposed for response surface investigation when some factors are qualitative. In addition to developing comprehensive computational studies of existing methods, new methods are proposed with potentially important advantages. For example, the dissertation provides some of the first unbalanced screening experimental plans relevant to cases in which some combinations of settings have far higher costs than other combinations. For situations in which some factors are mixture components, e.g., %CO2, %Ar, %N, and other factors are process variables, the dissertation provides some of the first economically relevant experimental plans offering potentially substantial reductions in prediction errors. Also, the dissertation provides the first truly advisable experimental designs for many response surface cases in which some variables are qualitative. All new methods are derived from optimization formulations or "improvement systems design problems". In each case, the intent is to design the method using the objective or objectives that most directly describe the purpose of the improvement system. Also, the formulations build on the most realistic, concise assumption schemes in the applied statistics literature. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Design of Experiments; DOE; Fractional Factorial Design; Mixture Design; Response Surface Method; Response Surface Design; Qualitative Factor; Categorical Factor; Qualitative Variable; Categorical Variable

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APA (6^th Edition):

Chantarat, N. (2003). Modern design of experiments methods for screening and experimentations with mixture and qualitative variables. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056

Chicago Manual of Style (16^th Edition):

Chantarat, Navara. “Modern design of experiments methods for screening and experimentations with mixture and qualitative variables.” 2003. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056.

MLA Handbook (7^th Edition):

Chantarat, Navara. “Modern design of experiments methods for screening and experimentations with mixture and qualitative variables.” 2003. Web. 15 Jan 2021.

Vancouver:

Chantarat N. Modern design of experiments methods for screening and experimentations with mixture and qualitative variables. [Internet] [Doctoral dissertation]. The Ohio State University; 2003. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056.

Council of Science Editors:

Chantarat N. Modern design of experiments methods for screening and experimentations with mixture and qualitative variables. [Doctoral Dissertation]. The Ohio State University; 2003. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1064198056

The Ohio State University

17. Ferhatosmanoglu, Nilgun. Optimal design of experiments for emerging biological and computational applications.

Degree: PhD, Industrial and Systems Engineering, 2007, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867

► This dissertation explores two types of applications of applied statistics techniques to develop methods associated with bioinformatics and information retrieval. The first type relates to… (more)

▼ This dissertation explores two types of applications of applied statistics techniques to develop methods associated with bioinformatics and information retrieval. The first type relates to planning probably the most common type of genetics related experiment, i.e., co-hybridized microarray testing. The question addressed concerns how to deploy the samples to slides and select dye colors to improve the sensitivity and specificity without increasing the associated cost. A generalized A-optimality criterion called the expected squared errors of coefficient estimates (ESECE) is proposed to aid in experimental design selection. The proposed criterion also can be applied to any type of experimentation focused on parameter estimation. Heuristic methods to generate arrays using the proposed criterion are also suggested. The resulting “hybrid” designs constitute a compromise between the widely used “reference” designs and the “loop” designs. The proposed criterion and a study of 15,488 genes together suggest that reference designs are generally likely to foster more accurate estimation than loop designs. Also, the proposed “hybrid” designs likely offer further benefits in increased sensitivity and specificity with no added costs. The second type of application explored is the design of vector space search engines, which constitute perhaps the most common type of search technology in information retrieval. In this dissertation, two types of methods are explored separately and also combined to tune the selection of weights of the similarity distance function so that the search engine generates results of greater interest to users. The first type is so-called discrete choice analysis (DCA) methods to estimate the weights that putatively maximize the expected utility of users in the context of specific queries. The second type of method is the application of mixture modeling. Based on the fitting of specific types of mixture regression models, methods are proposed to enhance the expected user utility for a variety of queries. The DCA methods are illustrated using a news database and simulated users. The associated test problems provide an indication that the proposed methods could improve performance compared with the common strategy of applying equal weights for all semantic dimensions. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Engineering, Industrial; design of experiments; discrete choice analysis; mixture experiments; bioinformatics; microarrays; information retrieval; search engines; relevance feedback

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APA (6^th Edition):

Ferhatosmanoglu, N. (2007). Optimal design of experiments for emerging biological and computational applications. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867

Chicago Manual of Style (16^th Edition):

Ferhatosmanoglu, Nilgun. “Optimal design of experiments for emerging biological and computational applications.” 2007. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867.

MLA Handbook (7^th Edition):

Ferhatosmanoglu, Nilgun. “Optimal design of experiments for emerging biological and computational applications.” 2007. Web. 15 Jan 2021.

Vancouver:

Ferhatosmanoglu N. Optimal design of experiments for emerging biological and computational applications. [Internet] [Doctoral dissertation]. The Ohio State University; 2007. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867.

Council of Science Editors:

Ferhatosmanoglu N. Optimal design of experiments for emerging biological and computational applications. [Doctoral Dissertation]. The Ohio State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867

The Ohio State University

18. Schenk, Jason Robert. Meta-uncertainty and resilience with applications in intelligence analysis.

Degree: PhD, Industrial and Systems Engineering, 2008, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199129269

► Uncertainty plays a major and inevitable role in human decision-making. Meta-uncertainty about the uncertainty can also be important but it is generally less studied. Such… (more)

▼ Uncertainty plays a major and inevitable role in human decision-making. Meta-uncertainty about the uncertainty can also be important but it is generally less studied. Such meta-uncertainty has arisen in medical contexts as researchers and practitioners strive to improve conceptualizations of efficacy and mortality related data. Similar but less studied issues arise in the study of human conflicts, in related intelligence analysis, and in responding to business crises. For any given year, the chance of a new conflict arising between a pair of nation states or “dyad” is generally small even if those nations are “politically relevant” to each other. Predicting “no conflict” is almost always correct. Yet, the probabilities of conflict and their meta-uncertainty can be of great interest to military and civilian planners. This dissertation reviews and synthesizes methods available for both conflict probability prediction and meta-uncertainty estimation. It also proposes Bayesian mixture modeling approaches for these purposes and clarifies their potential advantages in relation to actual human conflict data. Intelligence analysis involves gathering and synthesizing a multitude of different data sources into a coherent explanation of events using adductive reasoning. The outputs often involve predicted probabilities of events, which are commonly used in real time briefings and after action reviews (AARs). Given a variety of time, data quality constraints, it can be important to convey the “rigor” or meta-uncertainty associated with probability prediction. For the context of intelligence analysis, this dissertation provides a visual and systematic framework for convey and document meta-uncertainty for intelligence analysis. This framework is based on the proposed “consequence likelihood” diagrams and can be referred to as “hypothesis scrubbing.” Resilience engineering offers new ways to conceptualize responsiveness and reserve capacity. This dissertation reviews and synthesizes many quantitative measures of system resilience. It also explores the application of a recently proposed “master” stress-strain model to evaluate response alternative to crises at a major call center. A main conclusion is that resilience engineering can be viewed as a response to high levels of meta-uncertainty. Also, the synthesis has illuminated a potentially important concept called the “graceful degradation angle” which rates the system’s ability for self-diagnosis. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Latent Dirichlet Allocation; prediction interval; stress-strain; consequence-likelihood

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APA (6^th Edition):

Schenk, J. R. (2008). Meta-uncertainty and resilience with applications in intelligence analysis. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1199129269

Chicago Manual of Style (16^th Edition):

Schenk, Jason Robert. “Meta-uncertainty and resilience with applications in intelligence analysis.” 2008. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199129269.

MLA Handbook (7^th Edition):

Schenk, Jason Robert. “Meta-uncertainty and resilience with applications in intelligence analysis.” 2008. Web. 15 Jan 2021.

Vancouver:

Schenk JR. Meta-uncertainty and resilience with applications in intelligence analysis. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199129269.

Council of Science Editors:

Schenk JR. Meta-uncertainty and resilience with applications in intelligence analysis. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199129269

The Ohio State University

19. Zheng, Ning. Discovering interpretable topics in free-style text: diagnostics, rare topics, and topic supervision.

Degree: PhD, Industrial and Systems Engineering, 2008, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199237529

► Massive databases with free-style text fields are a common feature of virtually all types of organizations from hospitals to aviation companies to governmental agencies.… (more)

▼ Massive databases with free-style text fields are a common feature of virtually all types of organizations from hospitals to aviation companies to governmental agencies. Perhaps the most promising approaches for intelligent, automatic text analysis are called "topic models". Yet, it is likely also true that all topic models generate at least some topics that do not correspond to anything human analysts understand and can act upon. In this dissertation, we begin by synthesizing the literature on text modeling and information retrieval. We argue that the research has evolved from focusing on fast search/document retrieval to creating interpretable models of entire corpora, i.e., databases. We also argue that the topic model literature has largely failed to address statistical issues relating to data limitations, rare topics, and the associated effects on topic model accuracy. Next, we clarify the limitations of the standard measure of topic model accuracy, perplexity, for cases in which topic interpretability and accuracy are important. Then, we propose new measures including the "KL percentage" that provide absolute evaluations of the accuracy or "informativeness" of all topics in the model. Computational experiments show that the proposed measures are more sensitive and give different data requirement estimates than perplexity. Then, to improve the interpretability of topics outputted from topic models, we propose using human-computer interaction (HCI) and integrating the results directly into the topic models. We introduce "anti-words" to capture negative relationships in which words do not belong to topics. Also, we propose two supervision methods, the probabilistic constraint (PC) method and the topic augmentation (TA) method, and demonstrate their benefits using numerical examples. Next, we propose the topic model process control (TMPC) approach for control charting systems characterized by free-style text. This approach identifies new trends and assignable causes and is based on chi-squared tests on the empirical topic percentages. Finally, we show the effectiveness of all methods for an Ohio-based company. Interesting rare, unexpected topics are discovered after supervision representing new classes of customers, and the TMPC method correctly indicates unusual activities and their root causes. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: topic models; Bayesian inference; Dirichlet processes; experimental design; hierarchical Dirichlet processes

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APA (6^th Edition):

Zheng, N. (2008). Discovering interpretable topics in free-style text: diagnostics, rare topics, and topic supervision. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1199237529

Chicago Manual of Style (16^th Edition):

Zheng, Ning. “Discovering interpretable topics in free-style text: diagnostics, rare topics, and topic supervision.” 2008. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199237529.

MLA Handbook (7^th Edition):

Zheng, Ning. “Discovering interpretable topics in free-style text: diagnostics, rare topics, and topic supervision.” 2008. Web. 15 Jan 2021.

Vancouver:

Zheng N. Discovering interpretable topics in free-style text: diagnostics, rare topics, and topic supervision. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199237529.

Council of Science Editors:

Zheng N. Discovering interpretable topics in free-style text: diagnostics, rare topics, and topic supervision. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199237529

The Ohio State University

20. Tseng, Shih-Hsien. Bayesian and Semi-Bayesian regression applied to manufacturing wooden products.

Degree: PhD, Industrial and Systems Engineering, 2008, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473

► In the mid-twentieth century, George Box and others argued convincingly that model misspecification errors or "bias" should dominate our thinking about planning experiments. The… (more)

▼ In the mid-twentieth century, George Box and others argued convincingly that model misspecification errors or "bias" should dominate our thinking about planning experiments. The reason was that in the problems they studied, bias had a far greater effect on accuracy than variance and efforts to mitigate the effects of bias generally helped with other errors, but not vice versa. Yet, fifty years later, researchers are just beginning to include bias considerations in the planning of experiments and the analysis of data. Perhaps the main complicating issue related to bias is the need to declare assumptions about the system "a priori" in the Bayesian fashion. We begin with a review of previous research about bias in experimental planning, including the definition of bias, assumptions about bias, the effect of bias, and several bias criteria that are used to obtain optimal designs and evaluate bias sensitivity, including for irregularly shaped design regions. Using regression to analyze "on-hand" data is more common than uses after planned experiments. Yet, in both cases, available approaches to estimate the "bias susceptibility" of the fitted model are limited. To provide diagnostic information about the bias and other summative information, we propose a model diagnostic that can be used like adjusted R2 but which explicitly accounts for bias errors. Unlike the Cp statistic, our proposed diagnostic can be estimated even if the bias sources are inestimable using ordinary least squares. The proposed diagnostic has the simple interpretation of being the expected plus or minus prediction errors in the units of the response. The diagnostic, which is based on Bayes' Theorem, can be used for ordinary least squares model selection giving rise to what we call "semi-Bayesian" regression. The key idea of the proposed diagnostic is to apply Bayesian regression to derive a picture of the bias sources for the fitted model. For this reason, we also provide a systematic analysis of the robustness of alternative Bayesian regression priors with the intent of providing generally applicable assumptions for "typical" regression applications. Two case studies involving furniture systems design are used to illustrate the proposed methods. Advisors/Committee Members: Allen, Theodore (Advisor).

Subjects/Keywords: Engineering, Industrial; multiple regression; bias; experimental design; stochastic search variable selection; SSVS

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APA (6^th Edition):

Tseng, S. (2008). Bayesian and Semi-Bayesian regression applied to manufacturing wooden products. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473

Chicago Manual of Style (16^th Edition):

Tseng, Shih-Hsien. “Bayesian and Semi-Bayesian regression applied to manufacturing wooden products.” 2008. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473.

MLA Handbook (7^th Edition):

Tseng, Shih-Hsien. “Bayesian and Semi-Bayesian regression applied to manufacturing wooden products.” 2008. Web. 15 Jan 2021.

Vancouver:

Tseng S. Bayesian and Semi-Bayesian regression applied to manufacturing wooden products. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473.

Council of Science Editors:

Tseng S. Bayesian and Semi-Bayesian regression applied to manufacturing wooden products. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473

The Ohio State University

21. Rajagopalan, Ravishankar. Response-Probability Model Analysis Plots With Applications in Engineering and Clinical Research.

Degree: PhD, Industrial and Systems Engineering, 2009, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1238137483

► In certain data analyses, the uncertainties associated with confounding or multicollinearity should result in a recommendation for additional data collection. In others cases, all… (more)

▼ In certain data analyses, the uncertainties associated with confounding or multicollinearity should result in a recommendation for additional data collection. In others cases, all plausible models lead to similar recommendations for action. This dissertation proposes a plotting technique to identify whether data limitations should preclude immediate recommendations. Specifically, the proposed “response-probability model analysis plots” (RPMAPs) show the probabilities of models being accurate versus box and whisker plots of the system responses of applying each model in related optimizations. The associated optimization formulations divide into three types. First, in the context of fractional factorial experiments, the decision-maker faces complete confounding of interactions but also the freedom to adjust all factor settings for response optimization which might involve engineering specification limits. The motivating applications here include real world data sets in injection molding and arc welding. The resulting recommendations range from collecting additional runs and exploring new factors. Second, the decision-maker is challenged by so-called “noise factors” that can only be controlled during experimentation but not during the normal system operations. Here, RPMAPs compare with Taguchi signal-to-noise ratio-based marginal plots. The motivating study here involves arc welding yield maximization and response-probability model analysis plots offer advantages in interpretability for multi-response optimization.The third type of optimization formulation relates to decision-making in the context of on-hand data. In these cases, the least squares estimates are generally not trustworthy because of multicollinearity and biasing interactions. As a result, the proposed response-probability model analysis plots are based on Bayesian shrinkage estimates. Also, the associated formulations are control policies because decision-makers can observe noise factor settings and tailor choices. The major case study in this dissertation relates to the system design of a university clinical research center (CRC). We show how exploratory data analysis techniques including response-probability model analysis plots (RPMAPs) result in suggestions for system improvement. These include spreading out patient arrivals via scheduling, the elimination of certain batch operations, and adjustments to the nurse scheduling system. These recommendations are confirmed using a high fidelity discrete event simulation model and lower fidelity approximate models that are described. Advisors/Committee Members: Allen, Theodore T. (Advisor).

Subjects/Keywords: Industrial Engineering; Response-Probability Model Analysis Plots; RPMAP; Fractional Factorials; Robust Design; On-hand Data; Clinical Research Center

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APA (6^th Edition):

Rajagopalan, R. (2009). Response-Probability Model Analysis Plots With Applications in Engineering and Clinical Research. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1238137483

Chicago Manual of Style (16^th Edition):

Rajagopalan, Ravishankar. “Response-Probability Model Analysis Plots With Applications in Engineering and Clinical Research.” 2009. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1238137483.

MLA Handbook (7^th Edition):

Rajagopalan, Ravishankar. “Response-Probability Model Analysis Plots With Applications in Engineering and Clinical Research.” 2009. Web. 15 Jan 2021.

Vancouver:

Rajagopalan R. Response-Probability Model Analysis Plots With Applications in Engineering and Clinical Research. [Internet] [Doctoral dissertation]. The Ohio State University; 2009. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1238137483.

Council of Science Editors:

Rajagopalan R. Response-Probability Model Analysis Plots With Applications in Engineering and Clinical Research. [Doctoral Dissertation]. The Ohio State University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1238137483

The Ohio State University

22. Brady, James E. Six sigma and the university:teaching, research, and meso-analysis.

Degree: PhD, Industrial and Systems Engineering, 2005, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1110222811

► Six Sigma was introduced by industry practitioners and consultants as a means to improve any given company’s competitive position. Its acceptance by industry has been… (more)

Subjects/Keywords: Engineering, Industrial; Six Sigma; Meso-analysis

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APA (6^th Edition):

Brady, J. E. (2005). Six sigma and the university:teaching, research, and meso-analysis. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1110222811

Chicago Manual of Style (16^th Edition):

Brady, James E. “Six sigma and the university:teaching, research, and meso-analysis.” 2005. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1110222811.

MLA Handbook (7^th Edition):

Brady, James E. “Six sigma and the university:teaching, research, and meso-analysis.” 2005. Web. 15 Jan 2021.

Vancouver:

Brady JE. Six sigma and the university:teaching, research, and meso-analysis. [Internet] [Doctoral dissertation]. The Ohio State University; 2005. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1110222811.

Council of Science Editors:

Brady JE. Six sigma and the university:teaching, research, and meso-analysis. [Doctoral Dissertation]. The Ohio State University; 2005. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1110222811

The Ohio State University

23. Taslim, Cenny. Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling.

Degree: PhD, Industrial and Systems Engineering, 2008, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112

► This dissertation proposes methods for steady state linear system identification for both forward cases in which prediction of outputs for new inputs are desired and… (more)

▼ This dissertation proposes methods for steady state linear system identification for both forward cases in which prediction of outputs for new inputs are desired and also inverse prediction of which inputs fostered measured outputs are needed. Special attention is given to genetic network modeling applications. Inverse prediction matters here because then one can predict the effective genetic perturbation associated with a new target drug compound or therapy. The primary application addressed in this dissertation is motivated by our on-going contributions related to Down syndrome which affects approximately 1 out of every 800 children. First, single shot experimentation and analysis to develop network models is considered. The discussion focuses on linear models because of the relevance of equilibrium conditions and the typical scarcity of perturbation data. Yet, deviations from linear systems modeling assumptions are also considered. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. In addition, topics for further research are suggested including the need to develop more numerically stable analysis methods, improved diagnostic procedures, sequential design and analysis procedures.Next, multi-stage design and analysis procedures are proposed for experimentation in which both forward and inverse predictions are relevant. Methods are proposed to derive desirable experimental plans for the next batch of tests based on both space filling and D-optimality. The space filling designs are intended to support both linear and nonlinear modeling while D-optimality methods are relatively model-dependent. Rigorous results related to linear optimality criteria are presented in relation to multi-criteria formulations of the forward-inverse problem. Computational results are presented based on the SOS pathway and inspired by an on-going study of the genetic network associated with Down syndrome. In the studied cases, the biologists added a multiple choice constraint to the formulation for their simplicity. Advisors/Committee Members: Allen, Theodore (Committee Chair), Lauria, Mario (Committee Co-Chair).

Subjects/Keywords: Bioinformatics; Biostatistics; Engineering; Operations Research; Statistics; Optimal Design of Experiments; D-Optimality; Inverse Regression; Transcriptional Networks; Statistical Simulation; System Identification; Steady-State; Forward-Inverse Modeling; Bayesian regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Taslim, C. (2008). Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112

Chicago Manual of Style (16^th Edition):

Taslim, Cenny. “Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling.” 2008. Doctoral Dissertation, The Ohio State University. Accessed January 15, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112.

MLA Handbook (7^th Edition):

Taslim, Cenny. “Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling.” 2008. Web. 15 Jan 2021.

Vancouver:

Taslim C. Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2021 Jan 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112.

Council of Science Editors:

Taslim C. Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112

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Open Access Theses and Dissertations