Current Search: optimization (x)
View All Items
(1 - 20 of 102)
Pages
- Title
- MULTIOBJECTIVE SIMULATION OPTIMIZATION USING ENHANCED EVOLUTIONARY ALGORITHM APPROACHES.
- Creator
-
Eskandari, Hamidreza, Geiger, Christopher, University of Central Florida
- Abstract / Description
-
In today's competitive business environment, a firm's ability to make the correct, critical decisions can be translated into a great competitive advantage. Most of these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective optimization problems typically try...
Show moreIn today's competitive business environment, a firm's ability to make the correct, critical decisions can be translated into a great competitive advantage. Most of these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective optimization problems typically try to scalarize the multiple objectives into a single objective. This transforms the original multiple optimization problem formulation into a single objective optimization problem with a single solution. However, the drawbacks to these traditional approaches have motivated researchers and practitioners to seek alternative techniques that yield a set of Pareto optimal solutions rather than only a single solution. The problem becomes much more complicated in stochastic environments when the objectives take on uncertain (or "noisy") values due to random influences within the system being optimized, which is the case in real-world environments. Moreover, in stochastic environments, a solution approach should be sufficiently robust and/or capable of handling the uncertainty of the objective values. This makes the development of effective solution techniques that generate Pareto optimal solutions within these problem environments even more challenging than in their deterministic counterparts. Furthermore, many real-world problems involve complicated, "black-box" objective functions making a large number of solution evaluations computationally- and/or financially-prohibitive. This is often the case when complex computer simulation models are used to repeatedly evaluate possible solutions in search of the best solution (or set of solutions). Therefore, multiobjective optimization approaches capable of rapidly finding a diverse set of Pareto optimal solutions would be greatly beneficial. This research proposes two new multiobjective evolutionary algorithms (MOEAs), called fast Pareto genetic algorithm (FPGA) and stochastic Pareto genetic algorithm (SPGA), for optimization problems with multiple deterministic objectives and stochastic objectives, respectively. New search operators are introduced and employed to enhance the algorithms' performance in terms of converging fast to the true Pareto optimal frontier while maintaining a diverse set of nondominated solutions along the Pareto optimal front. New concepts of solution dominance are defined for better discrimination among competing solutions in stochastic environments. SPGA uses a solution ranking strategy based on these new concepts. Computational results for a suite of published test problems indicate that both FPGA and SPGA are promising approaches. The results show that both FPGA and SPGA outperform the improved nondominated sorting genetic algorithm (NSGA-II), widely-considered benchmark in the MOEA research community, in terms of fast convergence to the true Pareto optimal frontier and diversity among the solutions along the front. The results also show that FPGA and SPGA require far fewer solution evaluations than NSGA-II, which is crucial in computationally-expensive simulation modeling applications.
Show less - Date Issued
- 2006
- Identifier
- CFE0001283, ucf:46905
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001283
- Title
- SPACECRAFT LOADS PREDICTIONVIA SENSITIVITY ANALYSIS AND OPTIMIZATION.
- Creator
-
braswell, tom, CATBAS, NECATI, University of Central Florida
- Abstract / Description
-
Discrepancies between the predicted responses of a finite element analysis (FEA) and reference data from test results arise for many reasons. Some are due to measurement errors, such as inaccurate sensors, noise in the acquisition system or environmental effects. Some are due to analyst errors precipitated by a lack of familiarity with the modeling or solver software. Still others are introduced by uncertainty in the governing physical relations (linear versus non-linear behavior), boundary...
Show moreDiscrepancies between the predicted responses of a finite element analysis (FEA) and reference data from test results arise for many reasons. Some are due to measurement errors, such as inaccurate sensors, noise in the acquisition system or environmental effects. Some are due to analyst errors precipitated by a lack of familiarity with the modeling or solver software. Still others are introduced by uncertainty in the governing physical relations (linear versus non-linear behavior), boundary conditions or the element material/geometrical properties. It is the uncertainty effects introduced by this last group that this study seeks to redress. The objective is the obtainment of model improvements that will reduce errors in predicted versus measured responses. This technique, whereby measured structural data is used to correct finite element model (FEM) errors, has become known as "model updating". Model updating modifies any or all of the mass, stiffness, and damping parameters of a FEM until an improved agreement between the FEA data and test data is achieved. Unlike direct methods, producing a mathematical model representing a given state, the goal of FE model updating is to achieve an improved match between model and test data by making physically meaningful changes. This study replaces measured responses by reference output obtained from a FEA of a small spacecraft. This FEM is referred to as the "Baseline" model. A "Perturbed" model is created from this baseline my making prescribed changes to the various component masses. The degree of mass variation results from the level of confidence existing at a mature stage of the design ii iii cycle. Statistical mean levels of confidence are assigned based on the type of mass of which there are three types: •Concentrated masses – nonstructural, lumped mass formulation (uncoupled) •Smeared masses – nonstructural mass over length or area, lumped mass formulation (uncoupled) •Mass density – volumetric mass, lumped mass formulation (uncoupled) A methodology is presented that accurately predicts the forces occurring at the interface between the spacecraft and the launch vehicle. The methodology quantifies the relationships between spacecraft mass variations and the interface accelerations in the form of sensitivity coefficients. These coefficients are obtained by performing design sensitivity /optimization analyses while updating the Perturbed model to correlate with the Baseline model. The interface forces are responses obtained from a frequency response analysis that runs within the optimization analysis. These forces arise due to the imposition of unit white noise applied across a frequency range extending up to 200 hertz, a cut-off frequency encompassing the lift-off energy required to elicit global mass response. The focus is on lift-off as it is characterized by base excitation, which produces the largest interface forces.
Show less - Date Issued
- 2007
- Identifier
- CFE0001733, ucf:47305
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001733
- Title
- A PARAMETRIC STUDY OF MESO-SCALE PATTERNS FOR AUXETIC MECHANICAL BEHAVIOR OPTIMIZATION.
- Creator
-
Schuler, Matthew C, Gordon, Ali P., University of Central Florida
- Abstract / Description
-
This thesis focuses on the development, parameterization and optimization of a novel meso-scale pattern used to induce auxetic behavior, i.e., negative Poisson�s ratio, at the bulk scale. Currently, the majority of auxetic structures are too porous to be utilized in conventional load-bearing applications. For others, manufacturing methods have yet to realize the meso-scale pattern. Consequently, new auxetic structures must be developed in order to confer superior thermo-mechanical responses...
Show moreThis thesis focuses on the development, parameterization and optimization of a novel meso-scale pattern used to induce auxetic behavior, i.e., negative Poisson�s ratio, at the bulk scale. Currently, the majority of auxetic structures are too porous to be utilized in conventional load-bearing applications. For others, manufacturing methods have yet to realize the meso-scale pattern. Consequently, new auxetic structures must be developed in order to confer superior thermo-mechanical responses to structures at high temperature. Additionally, patterns that take into account manufacturing limitations, while maintaining the properties characteristically attached to negative Poisson�s Ratio materials, are ideal in order to utilize the potential of auxetic structures. A novel auxetic pattern is developed, numerically analyzed, and optimized via design of experiments. The parameters of the meso-structure are varied, and the bulk response is studied using finite element analysis (FEA). Various attributes of the elasto-plastic responses of the bulk structure are used as objectives to guide the optimization process
Show less - Date Issued
- 2016
- Identifier
- CFH2000001, ucf:45595
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000001
- Title
- Optimization Analysis of a Simple Position Control System.
- Creator
-
Cannon, Arthur G., Towle, Herbert C., Engineering
- Abstract / Description
-
Florida Technological University College of Engineering Thesis; One of the problem areas of modern optimal control theory is the definition of suitable performance indices. This thesis demonstrates a rational method of establishing a quadratic performance index derived from a desired system model. Specifically, a first order model is used to provide a quadratic performance indix for which a second order system is optimized. Extension of the method to higher order systems, while requiring more...
Show moreFlorida Technological University College of Engineering Thesis; One of the problem areas of modern optimal control theory is the definition of suitable performance indices. This thesis demonstrates a rational method of establishing a quadratic performance index derived from a desired system model. Specifically, a first order model is used to provide a quadratic performance indix for which a second order system is optimized. Extension of the method to higher order systems, while requiring more computations, involves no additional theoretical complexities.
Show less - Date Issued
- 1972
- Identifier
- CFR0012011, ucf:53085
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFR0012011
- Title
- Six Degree of Freedom Dynamic Modeling of a High Altitude Airship and Its Trajectory Optimization Using Direct Collocation Method.
- Creator
-
Pierre-Louis, Pradens, Xu, Yunjun, Lin, Kuo-Chi, Das, Tuhin, University of Central Florida
- Abstract / Description
-
The long duration airborne feature of airships makes them an attractive solution for many military and civil applications such as long-endurance surveillance, reconnaissance, environment monitoring, communication utilities, and energy harvesting. To achieve a minimum energy periodic motion in the air, an optimal trajectory problem is solved using basic direct collocation methods. In the direct approach, the optimal control problem is converted into a nonlinear programming (NLP). Pseudo...
Show moreThe long duration airborne feature of airships makes them an attractive solution for many military and civil applications such as long-endurance surveillance, reconnaissance, environment monitoring, communication utilities, and energy harvesting. To achieve a minimum energy periodic motion in the air, an optimal trajectory problem is solved using basic direct collocation methods. In the direct approach, the optimal control problem is converted into a nonlinear programming (NLP). Pseudo-inverse and several discretization methods such as Trapezoidal and Hermite-Simpson are used to obtain a numerical approximated solution by discretizing the states and controls into a set of equal nodes. These nodes are approximated by a cubic polynomial function which makes it easier for the optimization to converge while ensuring the problem constraints and the equations of motion are satisfied at the collocation points for a defined trajectory. In this study, direct collocation method provides the ability to obtain an approximation solution of the minimum energy expenditure of a very complex dynamic problem using Matlab fmincon optimization algorithm without using Himiltonian function with Lagrange multipliers. The minimal energy trajectory of the airship is discussed and results are presented.
Show less - Date Issued
- 2017
- Identifier
- CFE0006779, ucf:51822
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006779
- Title
- OPTIMAL UPFC CONTROL AND OPERATIONS FOR POWER SYSTEMS.
- Creator
-
Wu, Xiaohe, Qu, Zhihua, University of Central Florida
- Abstract / Description
-
The content of this dissertation consists of three parts. In thefirst part, optimal control strategies are developed for UnifiedPower Flow Controller (UPFC) following the clearance of faultconditions. UPFC is one of the most versatile Flexible ACTransmission devices (FACTs) that have been implemented thus far.The optimal control scheme is composed of two parts. The first isan optimal stabilization control, which is an open-loop `Bang'type of control. The second is an suboptimal damping...
Show moreThe content of this dissertation consists of three parts. In thefirst part, optimal control strategies are developed for UnifiedPower Flow Controller (UPFC) following the clearance of faultconditions. UPFC is one of the most versatile Flexible ACTransmission devices (FACTs) that have been implemented thus far.The optimal control scheme is composed of two parts. The first isan optimal stabilization control, which is an open-loop `Bang'type of control. The second is an suboptimal damping control,which consists of segments of `Bang' type control with switchingfunctions the same as those of a corresponding approximate linearsystem. Simulation results show that the proposed control strategyis very effective in maintaining stability and damping outtransient oscillations following the clearance of the fault. Inthe second part, a new power market structure is proposed. The newstructure is based on a two-level optimization formulation of themarket. It is shown that the proposed market structure can easilyfind the optimal solutions for the market while takeing factorssuch as demand elasticity into account. In the last part, amathematical programming problem is formulated to obtain themaximum value of the loadibility factor, while the power system isconstrained by steady-state dynamic security constraints. Aniterative solution procedure is proposed for the problem, and thesolution gives a slightly conservative estimate of the loadibilitylimit for the generation and transmission system.
Show less - Date Issued
- 2004
- Identifier
- CFE0000052, ucf:46122
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000052
- Title
- THE RELATION BETWEEN OPTIMISM AND JOB PERFORMANCE: AN APPLIED SETTING.
- Creator
-
Davis, Mary, Wooten, William, University of Central Florida
- Abstract / Description
-
Research on cognitive ability measures consistently concludes that they are predictive of employee performance. While accounting for only about 9% of the variance in performance, however, cognitive ability measures are not sufficient. Alternative measures, such as measures of personality constructs, must be included to fully predict employee performance. The research on personality measures suggests that they are marginally predictive of employee performance. Research also suggests that...
Show moreResearch on cognitive ability measures consistently concludes that they are predictive of employee performance. While accounting for only about 9% of the variance in performance, however, cognitive ability measures are not sufficient. Alternative measures, such as measures of personality constructs, must be included to fully predict employee performance. The research on personality measures suggests that they are marginally predictive of employee performance. Research also suggests that predicative accuracy of personality measures can be enhanced when the measure is specific to the situation (i.e., stress measure are more predictive of performance in high stress situations compared to moderate or low stress situations). The current study compares a specific measure of a personality construct, the Seligman Attributional Style Questionnaire (a measure of optimism), with a broad, general measure of personality, the Gordon Personal Profile-Inventory, comparing jobs specifically requiring higher levels of optimism versus jobs that do not require high levels of optimism. The results suggest that the use of the SASQ under situationally specific conditions does not result in greater predictive accuracy that the more generic GPPI. In addition, neither measure resulted in significant correlations with employee performance. The study generally confirmed the literature on the limited utility of personality measures in predicting performance. It also raised questions about how situational specificity is operationized.
Show less - Date Issued
- 2006
- Identifier
- CFE0001262, ucf:46930
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001262
- Title
- A COMPARATIVE STUDY OF ANT COLONY OPTIMIZATION.
- Creator
-
Becker, Matthew, Mohapatra, Ram, University of Central Florida
- Abstract / Description
-
Ant Colony Optimization (ACO) belongs to a class of biologically-motivated approaches to computing that includes such metaheuristics as artificial neural networks, evolutionary algorithms, and artificial immune systems, among others. Emulating to varying degrees the particular biological phenomena from which their inspiration is drawn, these alternative computational systems have succeeded in finding solutions to complex problems that had heretofore eluded more traditional techniques. Often,...
Show moreAnt Colony Optimization (ACO) belongs to a class of biologically-motivated approaches to computing that includes such metaheuristics as artificial neural networks, evolutionary algorithms, and artificial immune systems, among others. Emulating to varying degrees the particular biological phenomena from which their inspiration is drawn, these alternative computational systems have succeeded in finding solutions to complex problems that had heretofore eluded more traditional techniques. Often, the resulting algorithm bears little resemblance to its biological progenitor, evolving instead into a mathematical abstraction of a singularly useful quality of the phenomenon. In such cases, these abstract computational models may be termed biological metaphors. Mindful that a fine line separates metaphor from distortion, this paper outlines an attempt to better understand the potential consequences an insufficient understanding of the underlying biological phenomenon may have on its transformation into mathematical metaphor. To that end, the author independently develops a rudimentary ACO, remaining as faithful as possible to the behavioral qualities of an ant colony. Subsequently, the performance of this new ACO is compared with that of a more established ACO in three categories: (1) the hybridization of evolutionary computing and ACO, (2) the efficacy of daemon actions, and (3) theoretical properties and convergence proofs.
Show less - Date Issued
- 2006
- Identifier
- CFE0001192, ucf:46844
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001192
- Title
- Energy-optimal Guidance of an AUV Under Flow Uncertainty and Fluid-Particle Interaction.
- Creator
-
De Zoysa Abeysiriwardena, Demuni Singith, Das, Tuhin, Kumar, Ranganathan, Elgohary, Tarek, Behal, Aman, University of Central Florida
- Abstract / Description
-
The work presented gives an energy-optimal solution to the guidance problem of an AUV. The presented guidance methods are for lower level control of AUV paths, facilitating existing global planning methods to be carried out comparatively more efficiently. The underlying concept is to use the energy of fluid flow fields the AUVs are navigating to extend the duration of missions. This allows gathering of comparatively more data with higher spatio-temporal resolution. The problem is formulated...
Show moreThe work presented gives an energy-optimal solution to the guidance problem of an AUV. The presented guidance methods are for lower level control of AUV paths, facilitating existing global planning methods to be carried out comparatively more efficiently. The underlying concept is to use the energy of fluid flow fields the AUVs are navigating to extend the duration of missions. This allows gathering of comparatively more data with higher spatio-temporal resolution. The problem is formulated for a generalized two dimensional uniform flow field given a fixed final time andfree end states. This allows the AUVs to navigate to certain spatial positions while maintaining the required temporal resolution of each segment of its mission. The simplistic way the problem is posed allows an analytical closed form solution of the Euler-Lagrange equations. Two dimensional thrust vectors are obtained as optimal control inputs. The control inputs are then incorporated into afeedback structure, allowing the particle to navigate in the presence of disturbance in the flow field. Further, the work also explores the influence of fluid-particle interaction on the control cost and behavior of the particle. The concept of changing the cost weights of the optimal cost formulation in situ has been introduced. Potential applications of the present concept are explored through anobstacle avoidance scenario. The optimal guidance methods are then adapted to non-uniform flow fields with quadratic and discontinuous spatial variation being the primary focus.
Show less - Date Issued
- 2018
- Identifier
- CFE0007169, ucf:52282
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007169
- Title
- ELECTIMIZE: A NEW EVOLUTIONARY ALGORITHM FOR OPTIMIZATION WITH APPLICATIONS IN CONSTRUCTION ENGINEERING.
- Creator
-
Abdel-Raheem, Mohamed, Khalafallah, Ahmed, University of Central Florida
- Abstract / Description
-
Optimization is considered an essential step in reinforcing the efficiency of performance and economic feasibility of construction projects. In the past few decades, evolutionary algorithms (EAs) have been widely utilized to solve various types of construction-related optimization problems due to their efficiency in finding good solutions in relatively short time periods. However, in many cases, these existing evolutionary algorithms failed to identify the optimal solution to several...
Show moreOptimization is considered an essential step in reinforcing the efficiency of performance and economic feasibility of construction projects. In the past few decades, evolutionary algorithms (EAs) have been widely utilized to solve various types of construction-related optimization problems due to their efficiency in finding good solutions in relatively short time periods. However, in many cases, these existing evolutionary algorithms failed to identify the optimal solution to several optimization problems. As such, it is deemed necessary to develop new approaches in order to help identify better-quality solutions. This doctoral research presents the development of a new evolutionary algorithm, named "Electimize," that is based on the simulation of the flow of electric current in the branches of an electric circuit. The main motive in this research is to provide the construction industry with a robust optimization tool that overcomes some of the shortcomings of existing EAs. In solving optimization problems using Electimize, a number of wires (solution strings) composed of a number of segments are fabricated randomly. Each segment corresponds to a decision variable in the objective function. The wires are virtually connected in parallel to a source of an electricity to represent an electric circuit. The electric current passing through each wire is calculated by substituting the values of the segments in the objective function. The quality of the wire is based on its global resistance, which is calculated using Ohm's law. The main objectives of this research are to 1) develop an optimization methodology that is capable of evaluating the quality of decision variable values in the solution string independently; 2) devise internal optimization mechanisms that would enable the algorithm to extensively search the solution space and avoid its convergence toward local optima; and 3) provide the construction industry with a reliable optimization tool that is capable of solving different classes of NP-hard optimization problems. First, internal processes are designed, modeled, and tested to enable the individual assessment of the quality of each decision variable value available in the solution space. The main principle in assessing the quality of each decision variable value individually is to use the segment resistance (local resistance) as an indicator of the quality. This is accomplished by conducting a sensitivity analysis to record the change in the resistance of a control wire, when a certain decision variable value is substituted into the corresponding segment of the control wire. The calculated local resistances of all segments of a wire are then normalized to ensure that their summation is equal to the global wire resistance and no violation is made of Kirchhoff's rule. A benchmark NP-hard cash flow management problem from the literature is attempted to test and validate the performance of the developed approach. Not only was Electimize able to identify the optimal solution for the problem, but also it identified ten alternative optimal solutions, outperforming the existing algorithms. Second, the internal processes for the sensitivity analysis are designed to allow for extensive search of the solution space through the generation of new wires. Every time a decision variable value is substituted in the control wire to assess its quality, a new wire that might have a better quality is generated. To further test the capabilities of Electimize in searching the solution space, Electimize was applied to a multimodal 9-city travelling salesman problem (TSP) that had been previously designed and solved mathematically. The problem has 27 alternative optimal solutions. Electimize succeeded to identify 21 of the 27 alternative optimal solutions in a limited time period. Moreover, Electimize was applied to a 16-city benchmark TSP (Ulysses16) and was able to identify the optimal tour and its alternative. Further, additional parameters are incorporated to 1) allow for the extensive search of the solution space, 2) prevent the convergence towards local optima, and 3) increase the rate of convergence towards the global optima. These parameters are classified into two categories: 1) resistance related parameters, and 2) solution exploration parameters. The resistance related parameters are: a) the conductor resistivity, b) its cross-sectional area, and c) the length of each segment. The main role of this set of parameters is to provide the algorithm with additional gauging parameters to help guide it towards the global optima. The solution exploration parameters included a) the heat factor, and b) the criterion of selecting the control wire. The main role of this set of parameters is to allow for an extensive search of the solution space in order to facilitate the identification all the available alternative optimal solutions; prevent the premature convergence towards local optima; and increase the rate of convergence towards the global optima. Two TSP instances (Bayg29 and ATT48) are attempted and the results obtained illustrate that Electimize outperforms other EAs with respect to the quality of solutions obtained. Third, to test the capabilities of Electimize as a reliable optimization tool in construction optimization problems, three benchmark NP-hard construction optimization problems are attempted. The first problem is the cash flow management problem, as mentioned earlier. The second problem is the time cost tradeoff problem (TCTP) and is used as an example of static optimization. The third problem is a site layout planning problem (SLPP), and represents dynamic optimization. When Electimize was applied to the TCTP, it succeeded to identify the optimal solution of the problem in a single iteration using thirty solution strings, compared to hundreds of iterations and solution strings that were used by EAs to solve the same problem. Electimize was also successful in solving the SLPP and outperformed the existing algorithm used to solve the problem by identifying a better optimal solution. The main contributions of this research are 1) developing a new approach and algorithm for optimization based on the simulation of the phenomenon of electrical conduction, 2) devising processes that enable assessing the quality of decision variable values independently, 3) formulating methodologies that allow for the extensive search of the solution space and identification of alternative optimal solutions, and 4) providing a robust optimization tool for decision makers and construction planners.
Show less - Date Issued
- 2011
- Identifier
- CFE0003954, ucf:48698
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003954
- Title
- Optimization Approaches for Electricity Generation Expansion Planning Under Uncertainty.
- Creator
-
Zhan, Yiduo, Zheng, Qipeng, Vela, Adan, Garibay, Ivan, Sun, Wei, University of Central Florida
- Abstract / Description
-
In this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments,...
Show moreIn this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments, and need to stay in operation for a long time and accommodate many different scenarios in the future. The uncertainties we are addressing in this dissertation mainly include demands, electricity prices, investment and maintenance costs of power generation units. To address these future uncertainties in the decision-making process, this dissertation adopts two different optimization approaches: decision-dependent stochastic programming and adaptive robust optimization. In the decision-dependent stochastic programming approach, we consider the electricity prices and generation units' investment and maintenance costs being endogenous uncertainties, and then design probability distribution functions of decision variables and input parameters based on well-established econometric theories, such as the discrete-choice theory and the economy-of-scale mechanism. In the adaptive robust optimization approach, we focus on finding the multistage adaptive robust solutions using affine policies while considering uncertain intervals of future demands.This dissertation mainly includes three research projects. The study of each project consists of two main parts, the formulation of its mathematical model and the development of solution algorithms for the model. This first problem concerns a large-scale investment problem on both thermal and wind power generation from an integrated angle without modeling all operational details. In this problem, we take a multistage decision-dependent stochastic programming approach while assuming uncertain electricity prices. We use a quasi-exact solution approach to solve this multistage stochastic nonlinear program. Numerical results show both computational efficient of the solutions approach and benefits of using our decision-dependent model over traditional stochastic programming models. The second problem concerns the long-term investment planning with detailed models of real-time operations. We also take a multistage decision-dependent stochastic programming approach to address endogenous uncertainties such as generation units' investment and maintenance costs. However, the detailed modeling of operations makes the problem a bilevel optimization problem. We then transform it to a Mathematic Program with Equilibrium Constraints (MPEC) problem. We design an efficient algorithm based on Dantzig-Wolfe decomposition to solve this multistage stochastic MPEC problem. The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems' expansion planning.
Show less - Date Issued
- 2016
- Identifier
- CFE0006676, ucf:51248
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006676
- Title
- Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid.
- Creator
-
Golshani, Amir, Sun, Wei, Qu, Zhihua, Vosoughi, Azadeh, Zhou, Qun, Zheng, Qipeng, University of Central Florida
- Abstract / Description
-
Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This...
Show moreLarge power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decisionsupport system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC powerflow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraintsgeneration decomposition algorithms. The developed RDSS tool has been tested onthe modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security.
Show less - Date Issued
- 2017
- Identifier
- CFE0007284, ucf:52169
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007284
- Title
- A Fitness Function Elimination Theory for Blackbox Optimization and Problem Class Learning.
- Creator
-
Anil, Gautham, Wu, Annie, Wiegand, Rudolf, Stanley, Kenneth, Clarke, Thomas, Jansen, Thomas, University of Central Florida
- Abstract / Description
-
The modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox...
Show moreThe modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox optimization from which, we conclude that of these three desired characteristics, only two can be maximized by any algorithm.We put forward an alternate view of optimization where we use knowledge about the problem class and samples from the problem instance to identify which problem instances from the class are being solved. From this Elimination of Fitness Functions approach, an idealized optimization algorithm that minimizes sample counts over any problem class, given complete knowledge about the class, is designed. This theory allows us to learn more about the difficulty of various problems, and we are able to use it to develop problem complexity bounds.We present general methods to model this algorithm over a particular problem class and gain efficiency at the cost of specifically targeting that class. This is demonstrated over the Generalized Leading-Ones problem and a generalization called LO**, and efficient algorithms with optimal performance are derived and analyzed. We also tighten existing bounds for LO***. Additionally, we present a probabilistic framework based on our Elimination of Fitness Functions approach that clarifies how one can ideally learn about the problem class we face from the objective functions. This problem learning increases the performance of an optimization algorithm at the cost of abstraction.In the context of this theory, we re-examine the blackbox framework as an algorithm design framework and suggest several improvements to existing methods, including incorporating problem learning, not being restricted to blackbox framework and building parametrized algorithms. We feel that this theory and our recommendations will help a practitioner make substantially better use of all that is available in typical practical optimization algorithm design scenarios.
Show less - Date Issued
- 2012
- Identifier
- CFE0004511, ucf:49268
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004511
- Title
- A METHODOLOGY FOR MINIMIZING THE OSCILLATIONS IN SUPPLY CHAINS USING SYSTEM DYNAMICS AND GENETIC ALGORITHMS.
- Creator
-
LAKKOJU, RAMAMOORTHY, RABELO, LUIS, University of Central Florida
- Abstract / Description
-
Supply Chain Management (SCM) is a critically significant strategy that enterprises depend on to meet challenges that they face because of highly competitive and dynamic business environments of today. Supply chain management involves the entire network of processes from procurement of raw materials/services/technologies to manufacturing or servicing intermediate products/services to converting them into final products or services and then distributing and retailing them till they reach final...
Show moreSupply Chain Management (SCM) is a critically significant strategy that enterprises depend on to meet challenges that they face because of highly competitive and dynamic business environments of today. Supply chain management involves the entire network of processes from procurement of raw materials/services/technologies to manufacturing or servicing intermediate products/services to converting them into final products or services and then distributing and retailing them till they reach final customers. A supply chain network by nature is a large and complex, engineering and management system. Oscillations occurring in a supply chain because of internal and/or external influences and measures to be taken to mitigate/minimize those oscillations are a core concern in managing the supply chain and driving an organization towards a competitive advantage. The objective of this thesis is to develop a methodology to minimize the oscillations occurring in a supply chain by making use of the techniques of System Dynamics (SD) and Genetic Algorithms (GAs). System dynamics is a very efficient tool to model large and complex systems in order to understand their complex, non-linear dynamic behavior. GAs are stochastic search algorithms, based on the mechanics of natural selection and natural genetics, used to search complex and non-linear search spaces where traditional techniques may be unsuitable.
Show less - Date Issued
- 2005
- Identifier
- CFE0000683, ucf:46489
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000683
- Title
- EXPLORING RESILIENCE AND INDIVIDUAL DIFFERENCES.
- Creator
-
Thorne, Robin, Mottarella, Karen, University of Central Florida
- Abstract / Description
-
Few studies have investigated the relationships among resilience, optimism, and personality traits with U.S. college students; although some work has been done with Chinese university students. The current study explores the relationship between resilience, optimism and the Big Five personality traits. A sample of 251 undergraduate students completed the Connor-Davidson Resilience Scale (CD-RISC), the 9-item version of the Personal Optimism & Self-Efficacy Optimism Scale (POSE-E), and the NEO...
Show moreFew studies have investigated the relationships among resilience, optimism, and personality traits with U.S. college students; although some work has been done with Chinese university students. The current study explores the relationship between resilience, optimism and the Big Five personality traits. A sample of 251 undergraduate students completed the Connor-Davidson Resilience Scale (CD-RISC), the 9-item version of the Personal Optimism & Self-Efficacy Optimism Scale (POSE-E), and the NEO- Five Factor Inventory (NEO-FFI). Results indicate a significant positive relationship between resilience and optimism. The results also indicate positive significant relationships between resilience and extraversion, as well as resilience and conscientiousness. A significant negative significant relationship between resilience and neuroticism was found. The results of this study helpful identify characteristics of students who are at-risk following life stressors and traumas.
Show less - Date Issued
- 2015
- Identifier
- CFH0004838, ucf:45478
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004838
- Title
- Optimization Algorithms for Deep Learning Based Medical Image Segmentations.
- Creator
-
Mortazi, Aliasghar, Bagci, Ulas, Shah, Mubarak, Mahalanobis, Abhijit, Pensky, Marianna, University of Central Florida
- Abstract / Description
-
Medical image segmentation is one of the fundamental processes to understand and assess the functionality of different organs and tissues as well as quantifying diseases and helping treatmentplanning. With ever increasing number of medical scans, the automated, accurate, and efficient medical image segmentation is as unmet need for improving healthcare. Recently, deep learn-ing has emerged as one the most powerful methods for almost all image analysis tasks such as segmentation, detection,...
Show moreMedical image segmentation is one of the fundamental processes to understand and assess the functionality of different organs and tissues as well as quantifying diseases and helping treatmentplanning. With ever increasing number of medical scans, the automated, accurate, and efficient medical image segmentation is as unmet need for improving healthcare. Recently, deep learn-ing has emerged as one the most powerful methods for almost all image analysis tasks such as segmentation, detection, and classification and so in medical imaging. In this regard, this dissertation introduces new algorithms to perform medical image segmentation for different (a) imaging modalities, (b) number of objects, (c) dimensionality of images, and (d) under varying labelingconditions. First, we study dimensionality problem by introducing a new 2.5D segmentation engine that can be used in single and multi-object settings. We propose new fusion strategies and loss functions for deep neural networks to generate improved delineations. Later, we expand the proposed idea into 3D and 4D medical images and develop a "budget (computational) friendly"architecture search algorithm to make this process self-contained and fully automated without scarifying accuracy. Instead of manual architecture design, which is often based on plug-in and out and expert experience, the new algorithm provides an automated search of successful segmentation architecture within a short period of time. Finally, we study further optimization algorithms on label noise issue and improve overall segmentation problem by incorporating prior information about label noise and object shape information. We conclude the thesis work by studying different network and hyperparameter optimization settings that are fine-tuned for varying conditions for medical images. Applications are chosen from cardiac scans (images) and efficacy of the proposed algorithms are demonstrated on several data sets publicly available, and independently validated by blind evaluations.
Show less - Date Issued
- 2019
- Identifier
- CFE0007841, ucf:52825
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007841
- Title
- Multi-Sensor Optimization of the Simultaneous Turning and Boring Operation.
- Creator
-
Deane, Erick, Xu, Chengying, Gou, Jihua, Gordon, Ali, University of Central Florida
- Abstract / Description
-
To remain competitive in today's demanding economy, there is an increasing demand for improved productivity and scrap reduction in manufacturing. Traditional manufacturing metal removal processes such as turning and boring are still one of the most used techniques for fabricating metal products. Although the essential metal removal process is the same, new advances in technology have led to improvements in the monitoring of the process allowing for reduction of power consumption, tool wear,...
Show moreTo remain competitive in today's demanding economy, there is an increasing demand for improved productivity and scrap reduction in manufacturing. Traditional manufacturing metal removal processes such as turning and boring are still one of the most used techniques for fabricating metal products. Although the essential metal removal process is the same, new advances in technology have led to improvements in the monitoring of the process allowing for reduction of power consumption, tool wear, and total cost of production. Replacing used CNC lathes from the 1980's in a manufacturing facility may prove costly, thus finding a method to modernize the lathes is vital.This research focuses on Phase I and II of a three phase research project where the final goal is to optimize the simultaneous turning and boring operation of a CNC Lathe. From the optimization results it will be possible to build an adaptive controller that will produce parts rapidly while minimizing tool wear and machinist interaction with the lathe. Phase I of the project was geared towards selecting the sensors that were to be used to monitor the operation and designing a program with an architecture that would allow for simultaneous data collection from the selected sensors at high sampling rates. Signals monitored during the operation included force, temperature, vibration, sound, acoustic emissions, power, and metalworking fluid flow rates. Phase II of this research is focused on using the Response Surface Method to build empirical models for various responses and to optimize the simultaneous cutting process. The simultaneous turning and boring process was defined by the four factors of spindle speed, feed rate, outer diameter depth of cut, and inner diameter depth of cut. A total of four sets of experiments were performed. The first set of experiments screened the experimental region toiiidetermine if the cutting parameters were feasible. The next three set s of designs of experiments used Central Composite Designs to build empirical models of each desired response in terms of the four factors and to optimize the process. Each design of experiments was compared with one another to validate that the results achieved were accurate within the experimental region.By using the Response Surface Method optimal machining parameter settings were achieved. The algorithm used to search for optimal process parameter settings was the desirability function. By applying the results from this research to the manufacturing facility, they will achieve reduction in power consumption, reduction in production time, and decrease in the total cost of each part.
Show less - Date Issued
- 2011
- Identifier
- CFE0004098, ucf:49087
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004098
- Title
- Integrating Multiobjective Optimization with the Six Sigma Methodology for Online Process Control.
- Creator
-
Abualsauod, Emad, Geiger, Christopher, Elshennawy, Ahmad, Thompson, William, Moore, Karla, University of Central Florida
- Abstract / Description
-
Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today's businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives...
Show moreOver the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today's businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict.Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives.This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model (-) a well-known inventory management model (-) that is formally defined and specifies various process costs as objective functions. The proposed SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3?)-based and six sigma (6?)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense.
Show less - Date Issued
- 2013
- Identifier
- CFE0004968, ucf:49561
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004968
- Title
- DESIGN OPTIMIZATION OF SOLID ROCKET MOTOR GRAINS FOR INTERNAL BALLISTIC PERFORMANCE.
- Creator
-
Hainline, Roger, Nayfeh, Jamal, University of Central Florida
- Abstract / Description
-
The work presented in this thesis deals with the application of optimization tools to the design of solid rocket motor grains per internal ballistic requirements. Research concentrated on the development of an optimization strategy capable of efficiently and consistently optimizing virtually an unlimited range of radial burning solid rocket motor grain geometries. Optimization tools were applied to the design process of solid rocket motor grains through an optimization framework developed to...
Show moreThe work presented in this thesis deals with the application of optimization tools to the design of solid rocket motor grains per internal ballistic requirements. Research concentrated on the development of an optimization strategy capable of efficiently and consistently optimizing virtually an unlimited range of radial burning solid rocket motor grain geometries. Optimization tools were applied to the design process of solid rocket motor grains through an optimization framework developed to interface optimization tools with the solid rocket motor design system. This was done within a programming architecture common to the grain design system, AML. This commonality in conjunction with the object-oriented dependency-tracking features of this programming architecture were used to reduce the computational time of the design optimization process. The optimization strategy developed for optimizing solid rocket motor grain geometries was called the internal ballistic optimization strategy. This strategy consists of a three stage optimization process; approximation, global optimization, and highfidelity optimization, and optimization methodologies employed include DOE, genetic algorithms, and the BFGS first-order gradient-based algorithm. This strategy was successfully applied to the design of three solid rocket motor grains of varying complexity. The contributions of this work was the development and application of an optimization strategy to the design process of solid rocket motor grains per internal ballistic requirements.
Show less - Date Issued
- 2006
- Identifier
- CFE0001236, ucf:46929
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001236
- Title
- A METHODOLOGY TO STABILIZE THE SUPPLY CHAIN.
- Creator
-
Sarmiento, Alfonso, Rabelo, Luis, University of Central Florida
- Abstract / Description
-
In todayÂÂ's world, supply chains are facing market dynamics dominated by strong global competition, high labor costs, shorter product life cycles, and environmental regulations. Supply chains have evolved to keep pace with the rapid growth in these business dynamics, becoming longer and more complex. As a result, supply chains are systems with a great number of network connections among their multiple components. The interactions of the network components with respect...
Show moreIn todayÂÂ's world, supply chains are facing market dynamics dominated by strong global competition, high labor costs, shorter product life cycles, and environmental regulations. Supply chains have evolved to keep pace with the rapid growth in these business dynamics, becoming longer and more complex. As a result, supply chains are systems with a great number of network connections among their multiple components. The interactions of the network components with respect to each other and the environment cause these systems to behave in a highly nonlinear dynamic manner. Ripple effects that have a huge, negative impact on the behavior of the supply chain (SC) are called instabilities. They can produce oscillations in demand forecasts, inventory levels, and employment rates and, cause unpredictability in revenues and profits. Instabilities amplify risk, raise the cost of capital, and lower profits. To reduce these negative impacts, modern enterprise managers must be able to change policies and plans quickly when those consequences can be detrimental. This research proposes the development of a methodology that, based on the concepts of asymptotic stability and accumulated deviations from equilibrium (ADE) convergence, can be used to stabilize a great variety of supply chains at the aggregate levels of decision making that correspond to strategic and tactical decision levels. The general applicability and simplicity of this method make it an effective tool for practitioners specializing in the stability analysis of systems with complex dynamics, especially those with oscillatory behavior. This methodology captures the dynamics of the supply chain by using system dynamics (SD) modeling. SD was the chosen technique because it can capture the complex relationships, feedback processes, and multiple time delays that are typical of systems in which oscillations are present. If the behavior of the supply chain shows instability patterns, such as ripple effects, the methodology solves an optimization problem to find a stabilization policy to remove instability or minimize its impact. The policy optimization problem relies upon a theorem which states that ADE convergence of a particular state variable of the system, such as inventory, implies asymptotic stability for that variable. The stabilization based on the ADE requires neither linearization of the system nor direct knowledge of the internal structure of the model. Moreover, the ADE concept can be incorporated easily in any SD modeling language. The optimization algorithm combines the advantage of particle swarm optimization (PSO) to determine good regions of the search space with the advantage of local optimization to quickly find the optimal point within those regions. The local search uses a Powell hill-climbing (PHC) algorithm as an improved procedure to the solution obtained from the PSO algorithm, which assures a fast convergence of the ADE. The experiments showed that solutions generated by this hybrid optimization algorithm were robust. A framework built on the premises of this methodology can contribute to the analysis of planning strategies to design robust supply chains. These improved supply chains can then effectively cope with significant changes and disturbances, providing companies with the corresponding cost savings.
Show less - Date Issued
- 2010
- Identifier
- CFE0002986, ucf:47977
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002986
