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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 29 of 29
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis
Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis
Theses and Dissertations
In 2007, the Office of the Assistant Secretary of Defense for Sustainment pushed for the need to transition to a Condition Based Maintenance Plus (CBM ) initiative for weapon systems in the U.S. Department of Defense. The CBM initiative can help increase aircraft availability (AA) for the United States Air Force. There are many reasons where AA can be affected but one such issue is engine availability primarily due to oil issues. Within the CBM perspective, this study examines the risk of a jet engine failure due to an oil issue and attempts to predict an engines time until next …
Comparison Of Lightning Warning Radii Distributions, Michael M. Maestas
Comparison Of Lightning Warning Radii Distributions, Michael M. Maestas
Theses and Dissertations
Previous research investigating lightning warning radii about the Cape Canaveral space launch facilities have focused on reducing these radii from either 5 nautical miles (NM) to 4 NM or from 6 NM to 5 NM depending on the structures being protected. Some of these findings have suggested the possibility of both a seasonal difference (warm versus cold) and lightning detection events (cloud-to-ground lightning (CG) or total lightning (TL)) impacting these radii and associated risk levels. Utilizing the 2017-2020 data provided by the 45th Weather Squadron at Patrick Space Force Base via the Mesoscale Eastern Range Lightning Information System (MERLIN), this …
Simulating Autonomous Cruise Missile Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Kyle W. Goggins
Simulating Autonomous Cruise Missile Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Kyle W. Goggins
Theses and Dissertations
The increasingly sophisticated anti-access area denial (A2AD) threat imposed by the modern integrated air defense system (IADS), coupled with the decreasingly potent advantage provided by high-end stealth platforms, has prompted Air Force senior leaders to invest in radically changing the nature of air power for the year 2030 and beyond. A prominent element of this new vision is weapon swarming, which aims to address this challenge by overwhelming the IADS with huge numbers of low-cost, attritable aerial assets emboldened by autonomous capabilities. This research proposes a framework for classifying the different levels of autonomous capability along three independent dimensions—namely ability …
Wavelet Methods For Very-Short Term Forecasting Of Functional Time Series, Jared K. Nystrom
Wavelet Methods For Very-Short Term Forecasting Of Functional Time Series, Jared K. Nystrom
Theses and Dissertations
Space launch operations at Kennedy Space Center and Cape Canaveral Space Force Station (KSC/CCSFS) are complicated by unique requirements for near-real time determination of risk from lightning. Lightning forecast weather sensor networks produce data that are noisy, high volume, and high frequency time series for which traditional forecasting methods are often ill-suited. Current approaches result in significant residual uncertainties and consequentially may result in forecasting operational policies that are excessively conservative or inefficient. This work proposes a new methodology of wavelet-enabled semiparametric modeling to develop accurate and timely forecasts robust against chaotic functional data. Wavelets methods are first used to …
Statistically Defensible Wind Tunnel Models, Timothy A. Roche
Statistically Defensible Wind Tunnel Models, Timothy A. Roche
Theses and Dissertations
Wind tunnels are used to test scale-model air frames in order to collect aerodynamic data. The Subsonic Aerodynamic Research Laboratory (SARL) Wind Tunnel is a low speed wind tunnel located at Wright-Patterson Air Force Base. The SARL Wind Tunnel team approached AFIT for assistance in creating statistically defensible models for the conditions inside the wind tunnel. During a wind tunnel test, pressure sensors cannot be placed at the test model. Instead, pressure is measured by a pitot probe permanently mounted in the corner of the test chamber. The pressure at the model location is predicted from the measurements taken by …
An Examination Into Retention Behavior Of Air Force Female Officers, Jessica M. Astudillo
An Examination Into Retention Behavior Of Air Force Female Officers, Jessica M. Astudillo
Theses and Dissertations
Female retention rates in the US military have been considerably lower than that of their male counterparts for numerous years. In the Air Force, women represent 14 percent of officer ranks from O-5 level and above. Comparatively, the overall rate of women officers in service is 20 percent. Understanding the negative factors associated with the attrition rate of this group can help the Air Force leverage positive change. It may also influence adjustments that will increase the number of women serving, and improve diversity throughout both the officer and enlisted ranks. In this study, logistic regression and survival analysis are …
Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine
Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine
Faculty Publications
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in …
Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee
Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee
Theses and Dissertations
The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground …
Analysis And Forecasting Of The 360th Air Force Recruiting Group Goal Distribution, Tyler Spangler
Analysis And Forecasting Of The 360th Air Force Recruiting Group Goal Distribution, Tyler Spangler
Theses and Dissertations
This research utilizes monthly data from 2012-2017 to determine economic or demographic factors that significantly contribute to increased goaling and production potential in areas of the 360th Recruiting Groups. Using regression analysis, a model of recruiting goals and production is built to identify squadrons within the 360 RCGs zone that are capable of producing more or fewer recruits and the factors that contribute to this increased or decreased capability. This research identifies that a zones high school graduation rate, the number of recruiters, and the number of JROTC detachments in a zone are positively correlated with recruiting goals and that …
Analysis With Dynamic Bayesian Networks Compared To Simulation, Aaron J. Salazar
Analysis With Dynamic Bayesian Networks Compared To Simulation, Aaron J. Salazar
Theses and Dissertations
This research compares simulations to Dynamic Bayesian Networks in analyzing situations. The research applies models that have known output mean and variance. Queueing systems have theoretical values of the steady-state mean and variance for the number of entities in the system. Monte Carlo simulation development is broken down into two separate approaches: discrete-event simulation and time-oriented simulation. The discrete-event simulation uses pseudo-random numbers to schedule and trigger future events (i.e. customer arrivals and services) and is based on the generated objects.The time-oriented simulation utilizes fixed-width time intervals and updates the system state according to a stochastic process for the set …
Application Of Non-Rated Line Officer Attrition Levels And Career Field Stability, Christine L. Zens
Application Of Non-Rated Line Officer Attrition Levels And Career Field Stability, Christine L. Zens
Theses and Dissertations
The Air Force monitors the strength of its active duty officer force and attempts to achieve the difficult challenge of employing a diversity of talent among career specialties and experience levels. This study completes two objectives, predicting future manning levels for 23 career fields, and providing a statistical framework to assess the stability of these fields. The first part of the study applies regression and survival analysis to subpopulations within the active duty Air Force officer corps, and then aggregates them by year to forecast future personnel levels. Four career fields are considered, including Acquisitions (ACQ), Logistics (LOG), Support (SPT), …
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
Faculty Publications
Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based …
A Confidence Paradigm For Classification Systems, Nathan J. Leap
A Confidence Paradigm For Classification Systems, Nathan J. Leap
Theses and Dissertations
There is no universally accepted methodology to determine how much confidence one should have in a classifier output. This research proposes a framework to determine the level of confidence in an indication from a classifier system where the output is or can be transformed into a posterior probability estimate. This is a theoretical framework that attempts to unite the viewpoints of the classification system developer (or engineer) and the classification system user (or war-fighter). The paradigm is based on the assumptions that the system confidence acts like, or can be modeled as a value and that indication confidence can be …
Improving Mixed Variable Optimization Of Computational And Model Parameters Using Multiple Surrogate Functions, David Bethea
Improving Mixed Variable Optimization Of Computational And Model Parameters Using Multiple Surrogate Functions, David Bethea
Theses and Dissertations
This research focuses on reducing computational time in parameter optimization by using multiple surrogates and subprocess CPU times without compromising the quality of the results. This is motivated by applications that have objective functions with expensive computational times at high fidelity solutions. Applying, matching, and tuning optimization techniques at an algorithm level can reduce the time spent on unprofitable computations for parameter optimization. The objective is to recover known parameters of a flow property reference image by comparing to a template image that comes from a computational fluid dynamics simulation, followed by a numerical image registration and comparison process. Mixed …
A Multivariate Magnitude Robust Control Chart For Mean Shift Detection And Change Point Estimation, Ryan M. Harrell
A Multivariate Magnitude Robust Control Chart For Mean Shift Detection And Change Point Estimation, Ryan M. Harrell
Theses and Dissertations
Statistical control charts are often used to detect a change in an otherwise stable process. This process may contain several variables affecting process stability. The goal of any control chart is to detect an out-of-control state quickly and provide insight on when the process actually changed. This reduces the off-line time the quality engineer spends assigning causality. In this research, a multivariate magnitude robust chart (MMRC) was developed using a change point model and a likelihood-ratio approach. Here the process is considered in-control until one or more normally distributed process variables permanently and suddenly shifts to out-of-control, stable value. Using …
Improved Hyperspectral Image Testing Using Synthetic Imagery And Factorial Designed Experiments, Joseph P. Bellucci
Improved Hyperspectral Image Testing Using Synthetic Imagery And Factorial Designed Experiments, Joseph P. Bellucci
Theses and Dissertations
The goal of any remote sensing system is to gather data about the geography it is imaging. In order to gain knowledge of the earth's landscape, post-processing algorithms are developed to extract information from the collected data. The algorithms can be intended to classify the various ground covers in a scene, identify specific targets of interest, or detect anomalies in an image. After the design of an algorithm comes the difficult task of testing and evaluating its performance. Traditionally, algorithms are tested using sets of extensively ground truthed test images. However, the lack of well characterized test data sets and …
Optimization Of A Multi-Echelon Repair System Via Generalized Pattern Search With Ranking And Selection: A Computational Study, Derek D. Tharaldson
Optimization Of A Multi-Echelon Repair System Via Generalized Pattern Search With Ranking And Selection: A Computational Study, Derek D. Tharaldson
Theses and Dissertations
With increasing developments in computer technology and available software, simulation is becoming a widely used tool to model, analyze, and improve a real world system or process. However, simulation in itself is not an optimization approach. Common optimization procedures require either an explicit mathematical formulation or numerous function evaluations at improving iterative points. Mathematical formulation is generally impossible for problems where simulation is relevant, which are characteristically the types of problems that arise in practical applications. Further complicating matters is the variability in the simulation response which can cause problems in iterative techniques using the simulation model as a function …
An Investigation Of The Effects Of Correlation, Autocorrelation, And Sample Size In Classifier Fusion, Nathan J. Leap
An Investigation Of The Effects Of Correlation, Autocorrelation, And Sample Size In Classifier Fusion, Nathan J. Leap
Theses and Dissertations
This thesis extends the research found in Storm, Bauer, and Oxley, 2003. Data correlation effects and sample size effects on three classifier fusion techniques and one data fusion technique were investigated. Identification System Operating Characteristic Fusion (Haspert, 2000), the Receiver Operating Characteristic Within Fusion method (Oxley and Bauer, 2002), and a Probabilistic Neural Network were the three classifier fusion techniques; a Generalized Regression Neural Network was the data fusion technique. Correlation was injected into the data set both within a feature set (autocorrelation) and across feature sets for a variety of classification problems, and sample size was varied throughout. Total …
Theater-Level Stochastic Air-To-Air Engagement Modeling Via Event Occurrence Networks Using Piecewise Polynomial Approximation, David R. Denhard
Theater-Level Stochastic Air-To-Air Engagement Modeling Via Event Occurrence Networks Using Piecewise Polynomial Approximation, David R. Denhard
Theses and Dissertations
This dissertation investigates a stochastic network formulation termed an event occurrence network (EON). EONs are graphical representations of the superposition of several terminating counting processes. An EON arc represents the occurrence of an event from a group of (sequential) events before the occurrence of events from other event groupings. Events between groups occur independently, but events within a group occur sequentially. A set of arcs leaving a node is a set of competing events, which are probabilistically resolved by order relations. An important EON metric is the probability of being at a particular node or set of nodes at time …
Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin
Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin
Theses and Dissertations
Minimum distance estimate is a statistical parameter estimate technique that selects model parameters that minimize a good-of-fit statistic. Minimum distance estimation has been demonstrated better standard approaches, including maximum likelihood estimators and least squares, in estimating statistical distribution parameters with very small data sets. This research applies minimum distance estimation to the task of making time series predictions with very few historical observations. In a Monte Carlo analysis, we test a variety of distance measures and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when …
Computer-Based Methods For Constructing Two-Level Fractional-Factorial Experimental Designs With A Requirement Set, Steven L. Forsythe
Computer-Based Methods For Constructing Two-Level Fractional-Factorial Experimental Designs With A Requirement Set, Steven L. Forsythe
Theses and Dissertations
This dissertation developed four methodologies for computer-aided experimental design of two-level fractional factorial designs with requirement sets (DOE/RS). The requirement sets identify all the experimental factors and the appropriate interaction terms to be evaluated in the experiment. Taguchi graphs and similar manual methods provide techniques for solving the DOE/RS problem. Unfortunately, these methods are limited because they become difficult to use as the number of factors or interaction terms exceeds ten. This research showed that the DOE/RS problem belongs to a class of difficult-to-solve problems known as NP-Complete. It is the combinatorial nature of NP-Complete problems that causes them to …
A New Sequential Goodness Of Fit Test For The Three-Parameter Gamma Distribution With Known Shape Based On Skewness And Kurtosis, Chil Ho Park
Theses and Dissertations
This research presents a new sequential goodness of fit test for the three-parameter gamma distribution with a known shape. The test is accomplished by employing two new tests, sample skewness and sample kurtosis, sequentially as test statistics. Unlike the typical goodness of fit test, using parameter estimation methods such as maximum likelihood estimation and minimum distance estimation, this test using the two test statistics above does not involve a substantial degree of computational complexity. Large Monte Carlo simulation has been used to determine critical values and overall significance levels for all combinations of the two tests, and to conduct extensive …
A Comparison Of Circular Error Probable Estimators For Small Samples, Charles E. Williams
A Comparison Of Circular Error Probable Estimators For Small Samples, Charles E. Williams
Theses and Dissertations
Several previous studies investigated the performance of competing circular error probable (CEP) estimators for small samples. This estimation is important in ICBM analysis because, due to expense, there are a limited number of ICBM test launches. In the most recent previous study (1993), Tongue considered five CEP estimators in a simulation test, attempting to determine the behavior of these estimators for populations of various bias, ellipticity, correlation, and sample size. In this paper, we build on Tongue's findings in three ways: (1) The number of estimators compared is expanded to eight. (2) Different factors and factor levels are used. (3) …
Experiments In Aggregating Air Ordnance Effectiveness Data For The Tacwar Model, James E. Parker
Experiments In Aggregating Air Ordnance Effectiveness Data For The Tacwar Model, James E. Parker
Theses and Dissertations
An interactive MS Access&trademark; based application that aggregates the output of the SABSEL model for input into the TACWAR model is developed. The application was developed following efforts to create a functional approximation of the SABSEL data using neural networks, statistical networks, and traditional statistical techniques. These approximations were compared to a look-up table methodology on the basis of accuracy, (RMSE
A Robust Method Of Solving Nonlinear Boundary Value Problems Via Modified Compromise Programming, John L. Zornick
A Robust Method Of Solving Nonlinear Boundary Value Problems Via Modified Compromise Programming, John L. Zornick
Theses and Dissertations
This study is an extension of Ng's previous work in which goal programming was used to determine an approximate solution to a boundary value problem. This approach follows the same basic approach developed by Ng in which the method of collocation was recast as a compromise programming model. Hence, instead of solving a system of simultaneous nonlinear equations, one seeks a compromise solution which minimizes (in a weighted residual sense) a vector norm of the differential equation residuals. A difference in this approach is that it makes use of a genetic algorithm as the optimizing engine as opposed to the …
Response Surface Methodology As A Sensitivity Tool In Decision Analysis, David A. Meyers
Response Surface Methodology As A Sensitivity Tool In Decision Analysis, David A. Meyers
Theses and Dissertations
The purpose of this study is to evaluate response surface methodology as a sensitivity analysis tool in the area of decision analysis. The advent of low-cost personal computer software, such as DPLTM, has created an accessible tool with the ability to frame and solve influence diagrams for decision problems. This study provides a comparison of current sensitivity analysis techniques vs those made possible through response surface methodology (RSM). Sensitivity analysis alternatives are demonstrated on a decision problem concerning the evaluation of force structure options for the Department of Defense. Sensitivity analysis is performed on both one-way and two-way perturbations of …
A New Goodness-Of-Fit Test For The Gamma Distribution Based On Sample Spacings From Complete And Censored Samples, Huseyin Duman
A New Goodness-Of-Fit Test For The Gamma Distribution Based On Sample Spacings From Complete And Censored Samples, Huseyin Duman
Theses and Dissertations
This thesis studies a new goodness-of-fit test for the gamma distribution with known shape parameter. This test statistic, Z*, is based on spacings from complete or censored samples. The size of samples varied between 5 and 35. The critical value tables were generated for the Z* test statistic for complete and censored samples. The critical values were obtained for five different significance levels: 0.20 0.15, 0.10, 0.05, and 0.01. An extensive power study, containing 50,000 Monte Carlo runs was conducted using nine alternative distributions, Ha. It was observed that the Z* test statistic was more powerful against certain …
Estimation Of The Captive-Carry Survival Function For The Advanced Medium Range Air-To-Air Missile (Amraam), David R. Denhard
Estimation Of The Captive-Carry Survival Function For The Advanced Medium Range Air-To-Air Missile (Amraam), David R. Denhard
Theses and Dissertations
This thesis considers the problem of estimating the survival function of an item (probability that the item functions for a time greater than a given time t) from sampling data subject to partial right censoring (a portion of the items in the sampling data have not yet been observed to fail). Specifically the thesis describes several parametric and non-parametric statistical models that can be used when the sampling data is subject to partial right censoring. These models are applied to the case of estimating the captive-carry survival function of the AIM-120A Advanced Medium Range Air-to-Air Missile (AMRAAM).
Proactive Monitoring Of Performance In Stochastic Communication Networks, John C. C. Van Hove
Proactive Monitoring Of Performance In Stochastic Communication Networks, John C. C. Van Hove
Theses and Dissertations
This research proposes several models for placing bounds on the expected values of some dynamic performance measures for computer communication networks with failing components. These models provide an understanding of expected network performance that is useful in the process of proactive performance monitoring and also in defining level of service agreements with network users. There were three objectives for this research. The first objective was to extend some existing models of steady-state stochastic network performance to a dynamic network flow representation in order to capture the dynamic nature of proactive monitoring. The second objective was to convert the extended absolute …