The state of Texas is facing critical decisions that will greatly impact the preparedness of the state to meet future water demand. Consequently, during the 83rd Texas Legislative Session, state legislators proposed House Bill 4 (HB 4), a bill that if funded will provide an additional two billion dollars of funding for Texas water planning projects. Objectively evaluating and prioritizing projects would enable the efficient distribution of funding and minimize conflicts between water users. This project uses multi-criteria decision modeling to compare various evaluation criteria and decision preferences and prioritize proposed water management strategies in the 2012 State Water Plan. Combinations of project, regional, and legislative criteria are considered in eight decision scenarios. Projects are evaluated using Logical Decisions software and Microsoft Excel to calculate project utility and identify distribution strategies for funding. Results of this study provide insight into regional and strategy funding biases. Additionally, the decision model analyses highlight the effects of project prioritization on urban vs. rural and arid vs. humid Texas water conflicts. / text

Detailed holistic patient data is critical for healthcare organizations to better serve their patient populations. This information allows healthcare organizations to create a detailed and holistic record of a patient’s health. However, this large aggregation of personally identifiable patient data raises serious privacy and security concerns amongst patients. For this reason, patient concerns around the privacy and security of information retained by healthcare organizations must be addressed through the development of effective public policy. This research, therefore argues that any decision making process aimed at developing public policy dealing with patient data privacy and security concerns should not only address regulatory concerns, but also patient-centric values. To accomplish this task, multi-objective decision analytic techniques, with Nissenbaum’s (2004) contextual integrity as a normative framework are used. This is done to elicit patient-centric preferences to assist organizations and governmental institutions alike in dealing with their privacy and security concerns around patient data stored by Healthcare Systems.

Knowing what to sell, when to sell, and to whom to sell is essential buyer behavior insight to allocate scarce marketing resources efficiently and effectively. Applying the theory of relationship marketing (Morgan and Hunt 1994), this study seeks to investigate the link between commitment and trust and firm adoption of radically innovative information technology (IT). The construct of radical innovation is operationalized through the use of cloud computing. A review of the vast scholarly literature on radical innovation diffusion and adoption, and modeling techniques used to analyze buyer behavior is followed by empirical estimation of each of the radical innovation adoption questions of purchase timing, brand choice, and purchase amount. Then, the inefficiencies in the independent model process are highlighted, suggesting the need for an integrated model. Next, an integrated model is developed to link the purchase timing, brand choice, and purchase amount decisions. The essay concludes with insight for marketing practitioners on the strength of the factors of commitment and trust on adoption of radical innovation, an improved methodology for the business-to-business marketing literature, and potential further research paths.

Knowing what to sell, when to sell, and to whom to sell is essential buyer behavior insight to allocate scarce marketing resources efficiently and effectively. Applying the theory of relationship marketing (Morgan and Hunt 1994), this study seeks to investigate the link between commitment and trust and firm adoption of radically innovative information technology (IT). The construct of radical innovation is operationalized through the use of cloud computing. A review of the vast scholarly literature on radical innovation diffusion and adoption, and modeling techniques used to analyze buyer behavior is followed by empirical estimation of each of the radical innovation adoption questions of purchase timing, brand choice, and purchase amount. Then, the inefficiencies in the independent model process are highlighted, suggesting the need for an integrated model. Next, an integrated model is developed to link the purchase timing, brand choice, and purchase amount decisions. The essay concludes with insight for marketing practitioners on the strength of the factors of commitment and trust on adoption of radical innovation, an improved methodology for the business-to-business marketing literature, and potential further research paths.

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Materials are one of the areas that require special attention while creating a project's master plan as well as during the daily construction progress. The absence of materials when needed is one of the main causes of loss of productivity at a jobsite. Inefficient materials management can lead to an increase of 50% in work hours. As a result, a detailed plan for the materials management of each construction project is necessary. The critical role of materials management in the success of a construction project motivates the development of a new framework for the process of materials management for the construction industry, specifically the electrical construction industry. Materials management problems have a great impact on general contractors, but are more critical for specialty contractors such as electrical contractors. Based on the co-authors' experience, the construction industry has moved toward specialty contractors in the last decade to the point where at least 80% of the work performed on a typical construction contract is done by specialty contractors. General contractors have become, for the most part, project managers. Currently, materials management functions in the construction industry are often performed on a fragmented basis with minimal communication and no clearly established responsibilities among the parties involved. In addition, the collaboration required among departments has not been considered and implemented. This fragmentation creates gaps in information flow, which leads to delays in material ordering and receiving, expediting costs, excessive inventories of some items and project delays. However, model-based, computerized solutions to materials management problems are proliferating. Unfortunately, the typical electrical contractor may be overwhelmed by the technology required by these solutions and the challenges of implementing them into their business practices. A way out of this dilemma is presented by designing an industry-specific framework for the development of computerized decision support systems for the supply chains of the electrical contracting industry. Decision models are ever-present in the materials management processes of industries other than construction and have proven their worth in improving productivity and profitability. Knowledge-management concepts were applied to design an integrated, effective system of decision-support tools for materials-management decisions of an electrical contractor during the construction phase of a project. The framework developed is valuable in two fundamental ways. First, the framework identifies and describes all phases of materials management for an integrated, holistic view of all factors that affect the total cost of materials and material shortages. The research created detailed mappings of the essential decisions, decision models and data that are required to support supply-chain activities of construction contractors throughout a project life cycle. Second, the framework differentiates those steps in the materials management process that are straightforward applications of methods from those steps that are decisions. For these decisions, that are critical to the performance of the materials management process, we introduce the concept of a decision model and describe how such models can be incorporated into an advanced materials management system. This phase of the research developed a structured systems design of distributed, integrated decision support systems for materials management of the electrical contractor. The research derives the optimal integration of people, decision processes, decision support systems and data that are required to support efficient and effective systems for acquisition, procurement, transport, storage and allocation of material in the construction industry. / Ph. D.

<p dir="ltr">Appealing to individuals’ social identity is a powerful form of social influence, capable of changing the way people process information, the information they think about, and how they evaluate other people. This form of social influence can function through perceptions of normal behavior within a social group, in which members of a group interpret ambiguous information through the lens of what is considered to be normal in their ingroup. The Social Identity Decision Process hypothesis, based on Social Identity Theory and Probabilistic Persuasion Theory, suggests that group norms associated with a decider's social identity can alter the perceived importance of attributes or cues in a decision environment and the strategies that are used to make choices in situations in which the group identity is salient. Taking the U.S. political landscape as a context and examining Republican and Democrat social identities, norms from these political groups were expected to impact the attributes and strategies partisans use when choosing whether to have solar panels on a house. Two studies are reported that examined these effects through multi-attribute decision making, in which predefined decision process models assessed participant behavior to analyze which attributes best describe participants’ decision making.</p>

<p>  </p> <p>With rising concerns over commercial aviation’s contribution to global carbon emissions, there exists a tremendous pressure on the aviation industry to find advanced technological solutions to reduce its share of CO2 emissions. Single-aisle (or narrowbody) aircraft are the biggest contributors to CO2 emissions by number of operations, insisting a need to reduce / eliminate their aircraft-level fuel consumption as soon as possible. A potential solution for this is to operate fully-electric single-aisle aircraft; however, the limitations of the current (and predicted future) battery technology is forcing the industry to explore hybrid-electric aircraft as a possible mid-term solution.</p> <p>Modeling hybrid-electric aircraft comes with its own challenges due to the presence of two different propulsion sources – gas turbine engines (powered by Jet-A fuel) and electric motors (powered by batteries). Since traditional sizing approaches and legacy sizing tools do not seem to work well for hybrid-electric aircraft, this work presents a “flight-mechanics-based” conceptual sizing tool for hybrid-electric aircraft, set up as a Multidisciplinary Design Optimization (MDO) toolbox. Some of the key features of the sizing tool include concurrently sizing the electric motors and downsizing the gas turbine engines while meeting the one-engine-inoperative (OEI) and top-of-climb constraints, and re-sizing the fuselage to account for the volumetric constraints associated with required batteries.</p> <p>Current work considers a parallel hybrid-electric single-aisle aircraft with a 900 nmi design range, with electric power augmentation (with electric motors operating at full throttle) available only for the takeoff and climb segments when sizing the aircraft. Four hybrid-electric propulsion technology cases are considered, and the resulting hybrid-electric aircraft show 15.0% to 22.5% reduction in fuel burn compared to a Boeing 737-800 aircraft.</p> <p>Another challenge with modeling hybrid-electric aircraft is determining their off-design performance characteristics (considering a different payload or mission range, or both). This work presents an energy management tool – set up as a nonlinear programming optimization problem – to minimize the fuel burn for a payload-range combination by identifying the optimal combination of throttle settings for the gas turbine engines and the electric motors during takeoff, climb, and cruise, along with identifying an optimal flight path. The energy management tool enables fuel savings of at least of 2%, with actual savings ranging from 142.1 lbs to 276.1 lbs per trip for a sample route (LGA–ORD) at a 80% load factor.</p> <p>Although the hybrid-electric aircraft sizing and performance analysis studies show encouraging results about the potential reduction in carbon emissions at an aircraft level, the future fleet-level carbon emissions are not expected to reduce proportionally to these aircraft level emission reductions. This work predicts the fleet-level environmental impacts of future single-aisle parallel hybrid-electric aircraft by modeling the behavior of a profit-seeking airline (with a mixture of conventional all Jet-A fuel burning and hybrid electric aircraft in its fleet) using the Fleet-Level Environmental Evaluation Tool (FLEET). FLEET’s model-based predictions rely upon historically-based information about US-touching airline routes and passenger demand served by US flag-carrier airlines from the Bureau of Transportation Statistics to initiate model-based predictions of future demand, aircraft fleet mix, and aircraft operations. Using the aircraft performance coefficients from the energy management tool to represent the behavior of a single-aisle parallel hybrid-electric aircraft, the FLEET simulation predicts the changes in the fleet-wide carbon emissions due to the introduction of this new aircraft in an airline fleet in the year 2035. By 2055, FLEET results predict that the fleet-wide CO2 emissions with hybrid-electric aircraft in the fleet mix are at least 1.2% lower than the fleet-wide CO2 emissions of a conventional (all Jet-A fuel burning) aircraft-only airline. The rather limited reduction in emissions is an attribute of the reduced range capability and higher operating cost of the hybrid-electric aircraft (relative to a conventional aircraft of similar size). This causes the airline to change the usage, acquisition and retirement of its conventional aircraft when hybrid-electric aircraft are available; this is most notable to serve passenger demand on certain predominantly single-aisle service routes that cannot be flown by the future single-aisle hybrid-electric aircraft. </p>

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. <br ><br /> We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. <br /><br /> This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. <br /><br /> Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. <br ><br /> We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. <br /><br /> This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. <br /><br /> Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.