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Landslides in the Southern Appalachian Mountains of East Tennessee often activate and reactivate. Often triggered by high-intensity or prolonged rainfall, landslides are responsible for infrastructure damage, closure of transportation routes, and even fatality. The study area is defined by the New River Watershed which has high elevation and steep slopes cutting through State Route 116. The route has hairpin turns and has experienced damage from past landslide events. The geology here is mostly shale and sandstones with coal bedding throughout. Much of the soil consists of a fine-loamy texture. Most drainage occurs from the New River, fed by runoff from slopes into roadways. This area experiences heavy rainfall with a yearly average of 70 inches. Landcover consists of a mostly forested landscape with shrubs and grassland. In response to previous landslides, the Tennessee Department of Transportation (TDOT) recently repaired six areas within the route intercepted by recent landslides. Aside from the landslides near TDOT’s corridors, approximately 50 additional landslides have been found using Google Earth and LiDAR data. Landslide hotspots were identified using kernel density estimation and the nearest neighbor index. A heuristic landslide susceptibility model was prepared by weighing the ArcGIS layers: slope, soil particle, geology, curvature, elevation, distance from the stream, and land cover, in their contribution to the previous landslides. Results indicate that additional sites in Anderson and Morgan County should be studied further for potential landslide-related damage. The study will improve the proactive decisions of TDOT and justify timely monitoring, maintenance, and strategic protection of the route from slope hazards.

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This report examines the widely acceptable Heuristic and Exact procedures for solving the problem of project scheduling and control under constrained resources. Heuristic approaches are more practical, however they depend on the type of the project as well as the resources involved. Exact procedures are illustrated using an Integer Linear Programming formulation of the problem, and also solving it using the Branch and Bound Technique. Impracticality of the exact methods stews from the fact that the computations expand to an unmanageable amount.

Traditional critical path methods imply the assumption of unlimited availability of resources. Mathematical models and heuristic techniques are two alternatives that consider resource limitation to sequence the activities of a project. This research explores the consideration of project scheduling under resource constraints for the specific case of single resource, single project scheduling. A computer model called GENRES-II search model is developed using a modification of Brooks' algorithm to develop project schedules. The criteria used are various weighted combinations of ACTIM, ACTRES and ACTFOL. An improvement of GENRES-II solutions is obtained when the best set of GEN-II values is input to a computer model called COMSOAL simulation model. The criteria developed generates a large number of feasible solutions rapidly. The probability of generating optimal solutions is related to the size of the generated sample. Eight network cases were considered to validate both computer models. Special attention was given to those activities that were considered critical at a specific time. The number of resources available was increased to a new higher limit in order to schedule activities that became critical. The GENRES-II model was effective in finding project durations equal to or less than ACTIM, ACTRES, GENRES or ACTFOL. The COMSOAL model was found very effective in most of the cases in improving the GEN-II solutions.

Yes / This paper proposes the application of a hybrid genetic algorithm (GA) for scheduling storage tanks. The proposed approach integrates GAs and heuristic rule-based techniques, decomposing the complex mixed-integer optimization problem into integer and real-number subproblems. The GA string considers the integer problem and the heuristic approach solves the real-number problems within the GA framework. The algorithm is demonstrated for three test scenarios of a water treatment facility at a port and has been found to be robust and to give a significantly better schedule than those generated using a random search and a heuristic-based approach.

Scheduling is one of the most important issues in the planning of manufacturing systems. This research focuses on solving the test scheduling problem which arises in semiconductor manufacturing environment. Semiconductor wafer devices undergo a series of test processes conducted on computer-controlled test stations at various temperatures. A test process consists of both setup operations and processing operations on the test stations. The test operations occur in a specified order on the wafer devices, resulting in precedence constraints for the schedule. Furthermore, the assignment of the wafer devices to test stations and the sequence in which they are processed affects the time required to finish the test operations, resulting in sequence dependent setup times. The goal of this research is to develop a realistic model of the semiconductor wafer test scheduling problem and provide heuristics for scheduling the precedence constrained test operations with sequence dependent setup times. A mathematical model is presented and two heuristics are developed to solve the scheduling problem with the objective of minimizing the makespan required to test all wafer devices on a set of test stations. The heuristic approaches generate a sorted list of wafer devices as a dispatching sequence and then schedule the wafer lots on test stations in order of appearance on the list. An experimental analysis and two case studies are presented to validate the proposed solution approaches. In the two case studies, the heuristics are applied to actual data from a semiconductor manufacturing facility. The results of the heuristic approaches are compared to the actual schedule executed in the manufacturing facility. For both the case studies, the proposed solution approaches decreased the makespan by 23-45% compared to the makespan of actual schedule executed in the manufacturing facility. The solution approach developed in this research can be integrated with the planning software of a semiconductor manufacturing facility to improve productivity. / Master of Science

This descriptive qualitative study examines six eighth-grade students' thinking while solving probability problems. This study aimed to gather direct information on students' problem solving processes informed by the heuristics and biases framework. This study used purposive sampling (Patton, 1990) to identify eighth-grade students who were knowledgeable about probability and had reached the formal operational stage of cognitive development. These criterion were necessary to reduce the likelihood of students' merely guessing answers and important so that the researcher could distinguish between reasoning and intuition. The theoretical framework for this study was informed by Kahneman and Fredrick's (2002) recent revision to the heuristics and biases framework grounded in the research of Amos Tversky and Daniel Kahneman. Kahneman and Fredrick (2002) drew on dual process theory to explain systematic and predictable heuristic ways of thinking. Dual process theory hypothesizes that human thinking is divided into two different modes of processing. One mode, called System 1, is fast and linked to intuition, and the other, called System 2, is slow and linked to reasoning (Evans, 2008; Stanovich and West, 2000). Within dual process theory, System 1 thinking provides a credible system for explaining why people use heuristic thinking (Kahneman and Frederick, 2002). The recent revision to the heuristics and biases framework is focused on three heuristics, representativeness, conjunction fallacy, and availability. These three heuristics are believed to share the same mental process identified by Kahneman and Fredrick (2002), as the attribute substitution process. The clinical task based interview method was used in this study. This technique allowed the researcher to better observe and interact with the participants while exploring the students' probability thinking. The researcher also used think-aloud protocols to better reveal the organic thinking patterns of the students in real time (Ericsson and Simon, 1980; Fox, Ericsson, and Bets, 2010; Van Someren, Barnard, and Sandberg, 1994). The data from the interviews were analyzed using the constant comparison method (Glaser, 1965). This analysis revealed three categories that were combined with other analyses to create profiles for various thinking patterns observed by the researcher. The researcher identified patterns of thinking by students that were consistent with System 1 thinking and associated with the attribute substitution process (Kahneman and Fredrick, 2002). There were also situations in which students demonstrated ways of thinking consistent with System 2 thinking. However, unexpected ways of thinking were also identified by the researcher. For example, there were occasions when students substituted their fraction knowledge when solving probability problems and even seemed to equate probability with fractions. This type of thinking was referred to as the content substitution process in this study. This process occurred when students were using System 1 thinking as well as other types of thinking. In addition, the researcher observed students with thinking patterns that contained characteristics of both System 1 and System 2, which is referred to as slow intuition in this study. Slow intuition seemed to affect students' problem solving strategies as they wavered between multiple problem solving strategies that included either of the two substitution processes: attribute substitution and content substitution. This study contributes to the body of knowledge related to probabilistic thinking. In particular, this study informs our understanding of heuristic thinking used by eighth-grade students when solving probability problems. Further, teaching practices that draw on Fischbein's (1975, 1987) general notion of intuition might be developed and used to improve probability reasoning skills. These teaching practices target students that depend on the attribute substitution process and/or the content substitution process. Each of these heuristic ways of thinking may require different instructional techniques to help students develop more sound ways of thinking about probability. Regardless, teachers need to be informed of the extent that some students rely on their fraction knowledge when solving probability problems. / Ph. D.

This thesis presents a greedy search algorithm for maneuver-based motion planning of agile vehicles. In maneuver-based motion planning, vehicle maneuvers are solved offline and saved in a library to be used during motion planning. From this library, a tree of possible vehicle states can be generated through the search space. A depth-first, library-based algorithm called AD-Lib is developed and used to quickly provide feasible trajectories along the tree. AD-Lib combines greedy search techniques with hill climbing and effective backtracking to guide the search process rapidly towards the goal. Using simulations of a four-thruster hovercraft, AD-Lib is compared to existing suboptimal search algorithms in both known and unknown environments with static obstacles. AD-Lib is shown to be faster than existing techniques, at the expense of increased path cost. The motion planning strategy of AD-Lib along with a switching controller is also tested in an environment with dynamic obstacles. / Master of Science

Agent-based models are computer simulations in which entities (agents) interact with each other and their environment according to local update rules. Local interactions give rise to global dynamics. These models can be thought of as in silico laboratories that can be used to investigate the system being modeled. Optimization problems for agent-based models are problems concerning the optimal way of steering a particular model to a desired state. Given that agent-based models have no rigorous mathematical formulation, standard analysis is difficult, and traditional mathematical approaches are often intractable. This work presents techniques for the analysis of agent-based models and for solving optimization problems with such models. Techniques include model reduction, simulation optimization, conversion to systems of discrete difference equations, and a variety of heuristic methods. The proposed strategies are novel in their application; results show that for a large class of models, these strategies are more effective than existing methods. / Ph. D.