Investigators have for some time been interested in identifying factors which influence visual search performance and the effect these factors have on the scanning process which is a major component of visual search. The factors receiving the most attention include the stimulus to response mapping (varied or consistent), display set size, and level of target-distractor discriminability. Information on how the clustering of stimuli and varying the probability of targets appearing in particular locations affects the search process has been reported. These factors have been shown to affect the scanning process, determining both search strategies and whether the process occurs in serial or parallel. Previous experiments have typically used visual fields smaller than that subtended by a single computer monitor and few have systematically manipulated all the factors mentioned. Actual search tasks frequently involve complex targets in much larger visual fields whose stimuli frequently vary in terms of: stimulus to response mappings; discriminability; display set sizes; and physical clustering. How these factors affect large field visual search is not understood as well as their role in smaller visual fields. Two experiments were run to determine how these factors effect large field visual search. In contrast to previous research, display set size effects were found in consistent mapping conditions in both experiments. Under certain discriminability conditions, consistent and varied mapping response times mimicked each other. Three different stimuli configurations were researched: uniform distribution, equal sized clusters, and clusters of greatly different size. The uniform distribution showed the longest response times in both experiments. When letter stimuli were used, there was no significant difference between response times in the other clustering conditions but when word stimuli were used, participants tended to search the smallest clusters first. A model of the process was developed. A very good fit was found between the model's predictions and observed results. Results from the model suggest that participants did not match their search strategies to the probabilities of the target appearing in a given location and that they failed to optimize their performance.

The management and allocation of resources in large-scale engineering and operational systems has become increasingly complex as a result of both advances in information technology and the presence of system uncertainty. Recent technological and communication advances have provided increased access to information in large-scale systems, but have also resulted in two primary issues for decision makers. First, due to the limited capacity of resources to process this information and perform system operations, careful selection must be made to identify the information sources (and associated tasks) that should be focused on when making resource allocation decisions. Second, the vast supply of information available to an agent presents the issue of identifying when enough information has been gathered to make a decision, or if the decision should be postponed until more information has been processed. These two issues result in the need to develop an approach that identifies both where and when to allocate these finite resources. Furthermore, uncertainties about information, future conditions, and market trends may exist in various resource-constrained situations, including engineering and system design processes, new technology development, enterprise systems, homeland security, emergency preparedness and response, global operations, and supply chain management. These uncertainties provide unique challenges when developing appropriate decision-making protocols and may require the integration of risk management techniques in the decision-making process. The primary focus of this research is to develop distributed decision policies that manage risk from multiple agent perspectives in a resource allocation system, and improve the overall performance utility for multiple agents and the system. The general approach used to govern these decisions is based on the concept of dynamic flexibility using options-based decision policies. The impact of managing system risk from a distributed decision-making perspective is evaluated with respect to improvements in both agent utilities and system properties while adhering to limited and finite capacity resource constraints. The first part of this research considers the decision policies of agents that act in a buyer-seller manner. This model introduces the concept of using options-based policies to manage risk for a task allocation decision, tests the impact of various system parameters, and incorporates the threat of task preemption. For this initial model, the resource views tasks as providing heterogeneous profit values and, therefore, develops a pricing incentive (or disincentive) scheme that encourages (or discourages) the current task from exercising its allocation option. This pricing scheme is designed to help the resource better manage its queue strategy and have more control over its rate of revenues. Because both the task and resource agents are making decisions to maximize their individual profits, utility is transferred between these agents and the resulting policies yield a zero-sum game. The second part of this research extends this flexible, options-based approach to hedge risks posed by the underlying system uncertainties for both the task and resource agents, and explores the endogenous relationships between agent decisions and the evolution of these uncertainties. The option of allocating a task to a resource for processing is valued from a multi-agent perspective and a risk-based, distributed decision-making policy is developed that improves the utilities of both the task and resource agents. Because the actions of decision makers may have an impact on the evolution of the underlying source of uncertainty, this relationship is modeled and a solution approach developed that converges to an equilibrium system state. The final result is a distributed decision-making policy that both responds to and controls the evolution of risk due to uncertainty in a resource allocation system. The theoretical advances obtained from this work can be extended to other problem domains within industrial engineering and operations research that would benefit from enhanced flexibility in the decision-making process.

Component obsolescence in the “high-tech” electronics industry has become a problem that cannot be ignored. Although recent attention has been given to component obsolescence, in general this issue is still dealt with reactively. This often results in sustainment of a long-life system such as ships, airplanes, power plant, and space based programs to be extremely costly. In addition, delayed schedules, extended downtimes, and technology lags are common occurrences in approaches that deal with obsolescence as it occurs. In wake of the rapid pace of technology innovation, turbulent markets and growing globalization, developing proactive approaches for dealing with obsolescence is a necessity for companies to remain competitive in the marketplace. Thus this dissertation focuses on three fundamental objectives that highlight the importance, provide new insight, and offer solutions to the problem of component obsolescence. The first objective concentrates on the importance of prediction models in determining the life cycle of a component. Obsolescence prediction is key in identifying the items most vulnerable and allows the company to effectively hedge against future uncertainty long before the problem arises. The second objective concentrates on proactive management approaches. This is accomplished through a case study with an industry partner. The purpose of an obsolescence management strategy is to ensure that, issues of obsolescence are anticipated, identified, analyzed, mitigated, reported, and dealt with in a cost effective and timely manner. In addition, it provides life cycle “support and guidance” to the management team. Dealing intelligently with flexibility and uncertainty is characteristic of the Real Options Pricing approach. Thus, the third objective concentrates on options pricing as a decision making tool for mitigating the effects of obsolescence. Making strategic decisions about when to invest, what technology to invest in, waiting until a future point in time when a new technology may be available, are all complex questions to answer. Real options pricing offers a novel approach to addressing issues of obsolescence in sustainment based technologies. Thus this dissertation demonstrates that obsolescence prediction, proactive management and mitigation and the use of real options is key in determining optimal decisions and staying competitive in the “high-tech” electronics industry.

The primary focus of this dissertation is the development of a distributed resource allocation method for large-scale systems. This dissertation consists of three parts. First, it develops a resource allocation model based on multi-linked bargaining markets. This model explicitly considers resource interactions through the use of non-separable utilities. The market consists of randomly matched buyers and sellers engaged in exclusive pairwise bargaining episodes. Sets of bargaining strategies are constructed and a distributed fixed point method is developed that allows efficient computation of the solution. This research proves that the proposed fixed point method converges to a unique solution and the underlying bargaining game is subgame perfect, resulting in an efficient allocation of resources when the market equilibrium is reached. Second, a distributed scientific computational infrastructure is developed for large-scale numerical experimentation and simulation. This environment is designed to simultaneously support the numerical computation of the distributed resource allocation model and the simulation of large resource allocation problems. This system is designed to work with grid and computational cluster resources using existing middleware. Scaling and performance properties of the infrastructure are studied for a variety of problem sizes. Finally, the resource allocation model is implemented on the distributed computational environment to study a large-scale sensor network problem, which is motivated by a real world application. The sensor network problem is used to highlight the need to provide a provably "good" solution within the "real-time" constraints.

This dissertation studies capacity investment decisions of a manufacturing firm facing high demand uncertainty in a make-to-order environment. Two important strategies are discussed to better respond to uncertainty in product demands; manufacturing/capacity flexibility and price flexibility. The first part considers high level models of pricing, production and capacity investment decisions of a firm offering substitutable products, where the price and production decisions are made when demand is realized. The study of the impact of product substitution on the firm's optimal investment strategy and the design of the production network leads to following conclusions: (1) Under realistic assumptions on the distribution of the demand functions, the optimal investment in flexible capacity tends to decrease as the products become closer substitutes. (2) By not taking into account product substitution at the investment stage, the firm significantly overestimates the optimal capacity investment levels. (3) Assigning substitutable products to different production plants increases the firm's expected profits with lower investment costs; this is because the system becomes more flexible since production can be transferred from one plant to the other by diverting the demand for one product to its substitute through pricing. The second part considers a more detailed capacity investment model where prices are fixed at the beginning of the season and the company faces short-term (period-to-period) demand variability associated with a make-to-order environment. An optimization-based simulation model is used to understand the impact of pricing and increased manufacturing flexibility on the performance of the supply chain. The results show that both manufacturing and pricing flexibilities significantly reduce the optimal investment levels while increasing the expected profits, but lead to higher production variability, system inventory and variability observed by suppliers upstream. As a second step, the model is modified to include outbound transportation costs and service constraints, which help to allocate the production to satisfy at least a certain percentage of demand in different demand regions. We show that considering service constraints significantly increases the minimum service level among different demand regions at the expense of higher system inventory and outbound transportation costs, especially when capacity is scarce.

The exchange of goods and services is affected by pricing policies, of which there are two broad categories: posted-price mechanisms (take-it-or-leave-it pricing), and price-discovery mechanisms (auction pricing). In the past, companies fixed prices of a product for a relatively long time period; i.e., prices were considered static. The reason for this strategy is found in the absence of accurate demand information, high transaction costs associated with changing prices, and huge investment in software and hardware to implement a dynamic pricing strategy. Although dynamically posted prices are also take-it-or-leave-it prices, the seller can dynamically change prices over time. The goal here is to balance demand and supply via dynamic pricing. Early adopters of dynamic pricing methods are commonly found in industries where the short-term capacity is difficult to change such as airlines, and hotels. Most of these industries operate in a centralized fashion, which allows prices to be changed at little or no cost. Contrasting to the former are retail-like industries, for whom short-term supply is more flexible but price changes are costly. The primary focus of this research is on the latter type industry. We consider a two-echelon supply chain with a single retailer and a single supplier for whom we develop a dynamic pricing policy, which optimizes the net profit in the presence of costly price changes. Besides that cost, we consider that under a dynamic pricing policy costs for buyers are uncertain. This uncertainty is addressed in an optimal order policy. In addition, repeated buyer seller interaction in a dynamic pricing setting requires the incorporation of buyer behavior and, therefore, it is included in the dynamic pricing policy. The results we obtain are twofold. First, we show that the use of dynamic pricing is beneficial to the individual node, but when applied to multiple nodes in a supply chain environment results in price amplification, or price bullwhip. Second, we observe that the cost for changing price is the unique contributor to "sticky prices", prices that remain fixed for a certain period of time.

Mixed-level factorial designs are experimental designs whose factors have different numbers of levels. These designs are very useful in experiments involving both qualitative and quantitative factors. One design approach is to run all possible combinations of the factor levels. However, as the number of factors or factor levels increases, the number of experiments increases dramatically. As a result, research has focused on developing orthogonal or near-orthogonal fractional factorial designs. The property of design balance, that the same number of runs is performed for each factor level, has been maintained in currently proposed designs. In some cases, maintaining balance requires too many experimental runs. The objective of this thesis is to develop fractional mixed-level factorial designs with economical run size that have desirable properties associated with near-balance and near-orthogonality. Two criteria are developed to assess the degree of near-balance for comparing and constructing designs. A modified J2-optimality criterion is used for comparing design near-orthogonality. These criteria are combined to assess different design alternatives. A genetic algorithm is then used to build designs with the most desirable combination of near-balance and near-orthogonality. / A Thesis submitted to the Department of Industrial Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Fall Semester, 2003. / November 21, 2003. / Design Of Experiments, Genetic Algorithm, Efficient Mixed-Level Designs, Balance Coefficient / Includes bibliographical references. / James R. Simpson, Professor Directing Thesis; Samuel A. Awoniyi, Committee Member; Joseph J. Pignatiello, Jr., Committee Member.

Since the first Cathode Ray Tube (CRT) TV was invented in 1927, display technology has progressed at a rapid speed, attracting tremendous attention and enormous resources. Liquid Crystal Display (LCD) is the most mature and popular technology in flat panel displays compared to CRT. Carbon nanotube backlight unit (CNT-BLU) was regarded as a strong contender to replace the cold cathode fluorescence lamp backlight unit (CCFL-BLU) in LCD. CNTs have been spotlighted as one of promising alternatives for new electron sources; small tips and large aspect ratios of CNTs allow for a large electric field enhancement that makes them ideal field emitters. In this research, nanotube network BuckyPaper was proposed to act as surface luminary source for BLU. The major objective of this research is to systematically characterize various field emission properties of BuckyPapers and various lengths of open-ended CNTs. Particularly, low turn-on voltage, high average current, high luminance, low power consumption, uniformity and longer life-span will be explored. VGCNF/SWNT in ratio 1:1 BuckyPaper film demonstrated the lowest turn-on voltage (0.623 V/um) and randomly dispersed SWNT BuckyPaper film showed the largest enhancement factor value (1062) among all BuckyPaper samples. The structure of emitters might be the major reason for the varying results from different compositions of BuckyPaper films. The 400 nm open-ended SWNT paste showed the lowest turn on voltage at 0.313 V/um and the largest enhancement factor value (3470). Open-ended SWNT paste exhibited the best I-V properties, but did not demonstrate acceptable durability. The effect of varying aspect ratio of open-ended SWNT will be investigated in the future. / A Thesis submitted to the Department of Industrial Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester, 2006. / Jun 30, 2006. / Nanotube BuckyPaper, Field Emission Properties, Du / Includes bibliographical references. / Ben Wang, Professor Directing Thesis; Zhiyong Liang, Committee Member; Chuck Zhang, Committee Member; Young-Bin Park, Committee Member.

Photovoltaic (PV) systems behave in an extraordinary and useful way: they react to light by transforming part of the incoming photons into electricity. Since PV installations require no fuel to operate, produce no pollution while producing electricity, they require little maintenance and are modular. These unique properties make the technology a cost and energy-effective means of permitting a wide range of solar- electric applications. Thus, innovative design and manufacture of PV devices and equipment, offers a new vision for consumers and business as to how power can be provided. One such application involves the design and manufacture of a Solar-Powered Floating Fountain, including a robust design of experiment, performance, cost and safety analyses, is presented in this thesis. The uniqueness of the fountain manufactured for this research is the capability for aeration of stagnant water bodies, such as lakes and ponds. Aeration of these water bodies by using solar power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. The fountain was built by the Research Center for Cutting-Edge Technologies (RECCET) and installed on a pond at Innovation Park, Tallahassee. The system is composed of two pumps, an air compressor, six solar panels, kit of batteries, a linear current booster, pressurized water tank and the float. Aeration was by exposing the water to air through the nozzles on the tank. By using this technique, the lake gained dissolved oxygen in the lowest layer, accomplishing the main objective of this project. Statistical analysis using a Split Plot design showed a significant uptake of both dissolved oxygen and destratification. / A Thesis submitted to the Department of Manufacturing and Industrial Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester, 2006. / February 20, 2006. / Solar Power, Split Plot Design, Aeration of Ponds / Includes bibliographical references. / Yaw A. Owusu, Professor Directing Thesis; Rodney Roberts, Committee Member; James Simpson, Committee Member.

Composite textiles have found widespread use and advantages in various industries and applications. The constant demand for high quality products and services requires companies to minimize their manufacturing costs, and delivery time in order to compete in general and niche marketplaces. Advanced manufacturing methods aim to provide economical methods of mold production. Creation of molding and tooling options for advanced composites encompasses a large portion of the fabrication time, making it a costly process and restraining factor. This research discusses a preliminary investigation into the use of soluble polymer compounds and additive manufacturing to fabricate soluble molds. These molds suffer from dimensional errors due to several factors, which have also been characterized. The basic soluble mold of a composite is 3D printed to meet the desired dimensions and geometry of holistic structures or spliced components. The time taken to dissolve the mold depends on the rate of agitation of the solvent. This process is steered towards enabling the implantation of optoelectronic devices within the composite to provide sensing capability for structural health monitoring. The shape deviation of the 3D printed mold is also studied and compared to its original dimensions to optimize the dimensional quality to produce dimensionally accurate parts. Mechanical tests were performed on compact tension (CT) resin samples prepared from these 3D printed molds and revealed crack propagation towards an embedded intact optical fiber. / A Thesis submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2015. / July 8, 2015. / Additive Manufacturing, Composite molding, Dimensional Analysis, Embedded sensors / Includes bibliographical references. / Tarik J. Dickens, Professor Directing Thesis; Richard Liang, Committee Member; Arda Vanli, Committee Member.