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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Multi-Objective Optimization Of Mixed-Variable, Stochastic Systems Using Single-Objective Formulations, Todd J. Paciencia
Multi-Objective Optimization Of Mixed-Variable, Stochastic Systems Using Single-Objective Formulations, Todd J. Paciencia
Theses and Dissertations
Many problems exist where one desires to optimize systems with multiple, often competing, objectives. Further, these problems may not have a closed form representation, and may also have stochastic responses. Recently, a method expanded mixed variable generalized pattern search/ranking and selection (MVPS-RS) and Mesh Adaptive Direct Search (MADS) developed for single-objective, stochastic problems to the multi-objective case by using aspiration and reservation levels. However, the success of this method in approximating the true Pareto solution set can be dependent upon several factors. These factors include the experimental design and ranges of the aspiration and reservation levels, and the approximation quality …
Search Techniques For Multi-Objective Optimization Of Mixed Variable Systems Having Stochastic Responses, Jennifer G. Walston
Search Techniques For Multi-Objective Optimization Of Mixed Variable Systems Having Stochastic Responses, Jennifer G. Walston
Theses and Dissertations
A research approach is presented for solving stochastic, multi-objective optimization problems. First, the class of mesh adaptive direct search (MADS) algorithms for nonlinearly constrained optimization is extended to mixed variable problems. The resulting algorithm, MV-MADS, is then extended to stochastic problems (MVMADS-RS), via a ranking and selection procedure. Finally, a two-stage method is developed that combines the generalized pattern search/ranking and selection (MGPS-RS) algorithms for single-objective, mixed variable, stochastic problems with a multi-objective approach that makes use of interactive techniques for the specification of aspiration and reservation levels, scalarization functions, and multi-objective ranking and selection. A convergence analysis for the …
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 …
Visualizing Early-Stage Breast Cancer Tumors In A Mammographic Environment Through A 3-Dimensional Mathematical Model, Christopher B. Bassham
Visualizing Early-Stage Breast Cancer Tumors In A Mammographic Environment Through A 3-Dimensional Mathematical Model, Christopher B. Bassham
Theses and Dissertations
In response to the insidious and deadly nature of breast cancer and the less-than-perfect detection ability of mammography, we develop a mathematical model as a foundation to the long-term goal of improving early breast cancer detection. By using modeling and simulation to construct an accurate breast cancer tumor model, we hope to solve the problems associated with mammogram misdiagnosis and, perhaps as a by-product, lend insight to tumor development dynamics. The final tumor model, written in MATLAB, provides realistic tumor growth and 2-dimensional visualization of 3-dimensional structures. Earlier modeling attempts capture slices of the tumor in the 2-dimensional growth spaces. …
An Approach For Tasking Allocated Combat Resources To Targets, David A. Koewler
An Approach For Tasking Allocated Combat Resources To Targets, David A. Koewler
Theses and Dissertations
Tasking allocated combat aircraft to strike targets is a complicated and time-consuming process for combat planners. Currently, the process of scheduling missions is a two to four day process. To be able to respond quickly to the changing conditions of the battlefield, the military needs to compress the time that this process requires. Despite efforts to develop computer-based tools to automatically plan missions, combat planners still manually perform most of the tasking and scheduling of aircraft and targets. Unfortunately some of the tools currently available are perceived to be complicated and time consuming to use by the planners. They also …
Parallel Implementation Of An Artificial Neural Network Integrated Feature And Architecture Selection Algorithm, Craig W. Rizzo
Parallel Implementation Of An Artificial Neural Network Integrated Feature And Architecture Selection Algorithm, Craig W. Rizzo
Theses and Dissertations
The selection of salient features and an appropriate hidden layer architecture contributes significantly to the performance of a neural network. A number of metrics and methodologies exist for estimating these parameters. This research builds on recent efforts to integrate feature and architecture selection for the multilayer perceptron. In the first stage of work a current algorithm is developed in a parallel environment, significantly improving its efficiency and utility. In the second stage, improvements to the algorithm are proposed. With regards to feature selection, a common random number (CRN) addition is proposed. Two new methods of architecture selection are examined, to …
Embedding A Reactive Tabu Search Heuristic In Unmanned Aerial Vehicle Simulations, Joel L. Ryan
Embedding A Reactive Tabu Search Heuristic In Unmanned Aerial Vehicle Simulations, Joel L. Ryan
Theses and Dissertations
We apply a Reactive Tabu Search (RTS) heuristic within a discrete event simulation to solve routing problems for Unmanned Aerial Vehicles (UAVs). Our formulation represents this problem as a multiple Traveling Salesman Problem with time windows (mTSPTW), with the objective of attaining a specified level of target coverage using a minimum number of vehicles. Incorporating weather and probability of UAV survival at each target as random inputs, the RTS heuristic in the simulation searches for the best solution in each realization of the problem scenario in order to identify those routes that are robust to variations in weather, threat, or …
The Application Of Sequential Convex Programming To Large-Scale Structural Optimization Problems, Todd A. Sriver
The Application Of Sequential Convex Programming To Large-Scale Structural Optimization Problems, Todd A. Sriver
Theses and Dissertations
Structural design problems are often modeled using finite element methods. Such models are often characterized by constraint functions that are not explicitly defined in terms of the design variables. These functions are typically evaluated through numerical finite element analysis (FEA). Optimizing large-scale structural design models requires computationally expensive FEAs to obtain function and gradient values. An optimization approach which uses the SCP sequential convex programming algorithm of Zillober, integrated as the optimizer in the Automated Structural Optimization System (ASTROS), is tested. The traditional approach forms an explicitly defined approximate subproblem at each design iteration that is solved using the method …