The Marine infantryman is carrying too much weight in combat. This thesis analyzes the trade-offs between individual load weights and the value that a Distributed Operations squad receives from the equipment its members carry. We use multiple objective decision analysis principles to help determine the coefficients for an integer linear programming model. The optimization model prescribes equipment assignment to individual positions that maximizes squad mission success while meeting target weights for the individual Marine. Our findings indicate that significant improvements can be made to the Marine's combat load weight and equipment composition. The optimization model provides the squad with a more efficient combination of equipment while reducing the average weight of the combat load by more than 19 percent for both the assault load and the approach march load. Also, by balancing the loads across the members of the squad, the model reduces the variation of weight across the squad positions from as much as 38 percent to less than 2 percent for all loads. By examining the trade space between equipment weight and equipment value, we assist in the creation of future Marine Corps doctrine by providing senior Marine leaders a starting point analysis for addressing this difficult problem.

We propose a new approach to optimize operations of hydro storage systems with multiple connected reservoirs whose operators participate in wholesale electricity markets. Our formulation integrates short-term intraday with long-term interday decisions. The intraday problem considers bidding decisions as well as storage operation during the day and is formulated as a stochastic program. The interday problem is modeled as a Markov decision process of managing storage operation over time, for which we propose integrating stochastic dual dynamic programming with approximate dynamic programming. We show that the approximate solution converges towards an upper bound of the optimal solution. To demonstrate the efficiency of the solution approach, we fit an econometric model to actual price and in inflow data and apply the approach to a case study of an existing hydro storage system. Our results indicate that the approach is tractable for a real-world application and that the gap between theoretical upper and a simulated lower bound decreases sufficiently fast. (authors' abstract)

This research explores the concepts of defensive programming as currently defined in the literature. Then these concepts are extended and more explicitly defined. The relationship between defensive programming, as presented in this research, and current programming practices is discussed and several benefits are observed. Defensive programming appears to benefit the entire software life cycle. Four identifiable phases of the software development process are defined, and the relationship between these four phases and defensive programming is shown. In this research, defensive programming is defined as writing programs in such a way that during execution the program itself produces communication allowing the programmer and the user to observe its dynamic states accurately and critically. To accomplish this end, the use of defensive programming snap shots is presented as a software development tool.

In the last twelve years there has been considerable research interest in mathematical programming approaches to the statistical classification problem, primarily because they are not based on the assumptions of the parametric methods (Fisher's linear discriminant function, Smith's quadratic discriminant function) for optimality. This dissertation focuses on the development of mathematical programming models for the three-group classification problem and examines the computational efficiency and classificatory performance of proposed and existing models. The classificatory performance of these models is compared with that of Fisher's linear discriminant function and Smith's quadratic discriminant function. Additionally, this dissertation investigates theoretical characteristics of mathematical programming models for the classification problem with three or more groups. A computationally efficient model for the three-group classification problem is developed. This model minimizes directly the number of misclassifications in the training sample. Furthermore, the classificatory performance of the proposed model is enhanced by the introduction of a two-phase algorithm. The same algorithm can be used to improve the classificatory performance of any interval-based mathematical programming model for the classification problem with three or more groups. A modification to improve the computational efficiency of an existing model is also proposed. In addition, a multiple-group extension of a mathematical programming model for the two-group classification problem is introduced. A simulation study on classificatory performance reveals that the proposed models yield lower misclassification rates than Fisher's linear discriminant function and Smith's quadratic discriminant function under certain data configurations. Data configurations, where the parametric methods outperform the proposed models, are also identified. A number of theoretical characteristics of mathematical programming models for the classification problem are identified. These include conditions for the existence of feasible solutions, as well as conditions for the avoidance of degenerate solutions. Additionally, conditions are identified that guarantee the classificatory non-inferiority of one model over another in the training sample.

Mathematicians and computational scientists are often limited in their ability to model complex phenomena by the time it takes to run simulations. This thesis will inform interested researchers on how the development of highly parallel computer graphics hardware and the compiler frameworks to exploit it are expanding the range of algorithms that can be explored on affordable commodity hardware. We will discuss the complexities that have prevented researchers from exploiting advanced hardware as well as the obstacles that remain for the non-computer scientist.

The problem of this study concerns the performance of students in computer programming following exposure to a computer-aided instruction (CAI) drill in sequencing and practice exercises. The purpose of this study was to determine whether or not student success rates improve in computer programming after completing CAI sequencing drills and practice exercises.

In this thesis is presented design and implementation of a system performing genetic programming in simply typed lambda calculus. Population initialization method based on term generating technique producing typed lambda terms in long normal form is introduced. This method is parameterized by simple search strategy. Several search strategies are presented, such as strategy for systematic generation or strategy corresponding to standard ramped half-and-half method. Another such a strategies called \textit{geometric} strategy is further examined in experiments and shown to have various desirable effects such as improved success rate, lesser time consumption and smaller average term size in comparison with standard ramped half-and-half generating method. Other performance enhancements are proposed and supported by experiments such as eta-normalization of generated individuals and @-tree representation of individuals. Abstraction elimination is utilized to enable use of simple tree- swapping crossover. Powered by TCPDF (www.tcpdf.org)

See Volume 1 for Abstract / GR2017

The need for sensor-based automatic motion planning and control of industrial robots in an unstructured environment is extensive. For example in-factory transportation, household chores, military applications, chemical, radioactive, and other applications dangerous to humans. Researchers are attempting to build systems capable of generating purposeful motion in highly uncertain co-nplex environments, using on-line information from robot sensors. An example of such a task would be moving a mobile robot or a manipulator arm from its starting position to a goal position in a scene with unknown arbitrarily shaped obstacles. Carrying out such tasks requires, first, sensors and related / GR2017

Crew schedules on the South African Transport Services are done by roster compilers at depots. A method that uses heuristic and mathematical programming algorithms was developed to replace existing hand methods. It is a two stage method that will use a microcomputer to assist roster compilers to draw up crew schedules. Initially timetables are subdivided into shifts and then they are combined into crew schedules. The solution, which produces a significant improvement compared with an existing crew schedule and an existing method, has been accepted in principle and computer programming has begun. In Appendix E another heuristic for the scheduling of league matches is described.