No description available.

No description available.

Nuclear govemment-oversight-agency (GOA) managers face increasing numbers of strategic decisions with fewer people to support their strategic decision processes. There is an increasing need to find new means of strategic decision support for nuclear GOA managers. This exploratory research contributes toward meeting this need by: (1) developing a set of contextual models of strategic decision making that link mental activities with information needs; (2) deriving a methodology to study the characteristics of the information needed by nuclear GOA managers when formulating strategic problems; and (3) demonstrating the methodology's use in drawing implications for designing decision tools. The literature reviewed in the areas of management. strategic management, decision making, decision tools, and information characteristics formed the foundation for the development of three contextual models of individual, strategic decision making. The most detailed model. the Elementary Mental Activities Model, links the mental activities of strategic decision making with types of information, providing a perspective for strategic information. An experiment was designed to engage nuclear GOA managers in realistic, strategic decision situations. Concurrent verbalizations yielded verbal protocols. Units of information used were identified according to the types of information-entities, attributes, values. relationships. and operators-in the Elementary Mental Activities Model. Unique units of information were identified during coding. "Frequency of use" and "use" by subject and by exercise were calculated for each unit of information. The information characteristics source. level of detail, class, internal/external, and relationship complexity were assigned to entities, relationships. or operators. Thirteen analyses of the data demonstrated the methodology's value for research in strategic decision processes, in strategic information, and in decision tools. All but one of the results were supported in the literature on strategic decision processes. The research led to the following, overall conclusions. 1. The Elementary Mental Activities Model represents the information use of nuclear GOA managers engaged in strategic decision processes and characterizes their strategic decision processes in terms of information use. 2. The methodology developed is useful in measuring the information use of nuclear GOA managers engaged in strategic decision processes for the purposes of determining information needs for designing decision tools. / Ph. D.

Improved strategic location of an Emergency Medical Service (EMS) facility can significantly increase EMS efficiency. Urban planners need to consider a location that satisfies multiple criteria in order to make an informed decision about a future EMS facility site. Apart from basic criteria such as parcel value and size, decision-makers need to consider area and population coverage from potential parcels. Geographic Information Systems (GIS) provide an adequate analysis environment for EMS facility siting as many considered criteria are of a spatial nature. However, urban planners making decisions about an EMS facility site often lack the necessary expertise to make full use of challenging GIS tools. In order to help urban planners in the analysis process, this research developed a Spatial Decision Support System (SDSS) for EMS facility siting. The system was developed in ESRI ArcGIS (9.3) using the Visual Basic for Applications (VBA) programming environment. The objective of the system was to integrate spatial data, analysis, and visualization in a single system to help users evaluate a facility siting problem. The system's performance was tested using data for the Town of Blacksburg, VA. In addition, the system was evaluated by local planners and GIS staff with experience in EMS facility siting. Planners agreed the system enables more comprehensive and straightforward use of GIS for EMS facility siting analysis than other available siting tools. Potential improvements include a simpler user interface, synthesis of geoprocessing techniques, reduction of analysis time through automation, and better decision-making by improved visualization of results. / Master of Science

No description available.

<p> The constantly changing and dynamic nature of medical knowledge has proven to be challenging for healthcare professionals. Due to reliance on human knowledge the practice of medicine in many cases is subject to errors that endanger patients' health and cause substantial financial loss to both public and governmental health sectors. Computer based clinical guidelines have been developed to help healthcare professionals in practicing medicine. Currently, the decision making steps within most guideline modeling languages are limited to the evaluation of basic logic expressions. On the other hand, data mining analyses aim at building descriptive or predictive mining models that contain valuable knowledge; and researchers in this field have been active to apply data mining techniques on health data. However, this type of knowledge can not be represented using the current guideline specification standards.</p> <p> In this thesis, we focus is on encoding, sharing and finally using the results obtained from a data mining study in the context of clinical care and in particular at the point of care. For this purpose, a knowledge management framework is proposed that addresses the issues of data and knowledge interoperability. Standards are adopted to represent both data and data mining results in an interoperable manner; and then the incorporation of data mining results into guideline-based Clinical Decision Support Systems is elaborated. A prototype tool has been developed as a part of this thesis that serves as the proof of concept which provides an environment for clinical guideline authoring and execution. Finally three real-world clinical case studies are presented.</p> / Thesis / Master of Applied Science (MASc)

Over the last two decades, wireless technology has become ubiquitous in the United States and other developed countries. Consumer devices such as AM/FM radios, cordless and cellular telephones, pagers, satellite televisions, garage door openers, and television channel changers are just some of the applications of wireless technology. More recently, wireless computer networking has seen increasing employment. A few reasons for this move toward wireless networking are improved electronics transmitters and receivers, reduced costs, simplified installation, and enhanced network expandability. The objective of the study is to generate understanding of the planning inherent in a broadband, fixed wireless telecommunication network and to implement that knowledge into an SDSS. Intermediate steps toward this goal include solutions to both fixed wireless point-to-multipoint (PMP) and fixed wireless mesh networks, which are developed and incorporated into the SDSS. This study explores the use of a Spatial Decision Support System (SDSS) for broadband fixed wireless connectivity to solve the wireless network planning problem. The spatial component of the DSS is a Geographic Information System (GIS), which displays visibility for specific tower locations. The SDSS proposed here incorporates cost, revenue, and performance capabilities of a wireless technology applied to a given area. It encompasses cost and range capabilities of wireless equipment, the customers' propensity to pay, the market penetration of a given service offering, the topology of the area in which the wireless service is proffered, and signal obstructions due to local geography. This research is both quantitative and qualitative in nature. Quantitatively, the wireless network planning problem may be formulated as integer programming problems (IP). The line-of-sight restriction imposed by several extant wireless technologies necessitates the incorporation of a GIS and the development of an SDSS to facilitate the symbiosis of the mathematics and geography. The qualitative aspect of this research involves the consideration of planning guidelines for the general wireless planning problem. Methodologically, this requires a synthesis of the literature and insights gathered from using the SDSS above in a what-if mode. / Ph. D.

The aim of this thesis is to carry out research into the application of experimental design in an activity-based environment; specifically into the methodology and potential application of experimental design and analysis as an activity-based management decision support tool in the general manufacturing, systems engineering and logistics engineering fields. This thesis will detail the author's research into the decision support framework that will be introduced in Chapter 1.0 and developed during the thesis. Chapter 2.0 will introduce activity-based management and develop a systems dynamics model of a US industrial enterprise. Experimental design and analysis will be introduced in Chapter 3.0. The development of a knowledge-based expert system (KBES) trainer called DESIGNS.ART will be covered in Chapter 4.0, and' a copy of the program has been included in Appendix A This KBES trainer has been designed to train engineers in US industry in the application of experimental design and analysis. Chapter 5.0 discusses the potential application of experimental design and analysis in each of the three fields. Chapter 6.0 combines experimental design and analysis with activity-based management by fully developing the activity-based management (ABM) framework introduced in Chapter 1.0. Chapter 6.0 includes a hypothetical case study of the ABM framework in operation. Chapter 7.0 proposes that this ABM framework be integrated into a US firm which has implemented activity-based costing. This research into the integration of experimental design and analysis with activity-based management in systems engineering, logistics engineering and general manufacturing is original, and it results in a contribution to both the systems engineering, logistics engineering and the general manufacturing fields. / Master of Science

Yes / Tunnel maintenance requires complex decision making, which involves pathology diagnosis and risk assessment, to ensure full safety while optimising maintenance and repair costs. A Decision Support System (DSS) can play a key role in this process by supporting the decision makers in identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key aspect is to identify which spatial stretches within a tunnel contain pathologies of similar kinds within neighbouring tunnel segments. This paper presents PADTUN, a novel intelligent decision support system that assists with pathology diagnosis and assessment of tunnels with respect to their disorders and diagnosis influencing factors. It utilises semantic web technologies for knowledge capture, representation, and reasoning. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. In addition, an intelligent mechanism is presented which exploits abstraction and inference capabilities. Thus PADTUN provides the world’s first semantically based intelligent DSS for tunnel maintenance. PADTUN was developed by an interdisciplinary team of tunnel experts and knowledge engineers in real-world settings offered by the NeTTUN EU Project. An evaluation of the PADTUN system is performed using real-world tunnel data and diagnosis tasks. We show how the use of semantic technologies allows addressing the complex issues of tunnel pathology inferencing, aiding in, and matching transportation experts’ expectations of decision support. The methodology is applicable to any linear transport structures, offering intelligent ways to aid with complex decision processes related to diagnosis and maintenance. / This work was part of the NeTTUN project, funded by the EC 7th Framework under Grant Agreement 280712.

In order to cope with the turbulent environments in digital age, an enterprise should response to the changes quickly. Therefore, an enterprise must improve her ability of real-time decision-making. One way to increase the competence of real-time decision-making is to use Real-Time Decision Support Systems (RTDSS). A key feature for a Decision Support Systems (DSS) to successfully support real-time decision-making is to help decision-makers selecting the best models within deadline. This study focuses on developing methods to support the mechanism of model selection in DSS. There are five results in this study. Firstly, we have developed a time-based framework to evaluate models. This framework can help decision-makers to evaluate the quality and cost of model solutions. Secondly, based on the framework of models evaluation, we also developed three models selection strategies. These strategies can help decision-makers to select the best model within deadline. Thirdly, according the definitions of parameter value precision and model solution precision in this study, we conduct a simulation analysis to understand the impacts of the precision of parameter values to the precision of a model solution. Fourthly, in order to understand the interaction among the model selection variables, we also simulate the application of model selection strategies. The results of simulation indicate our study can support models selection well. Finally, we developed a structure-based model retrieval method to help decision-makers find alternative models from model base efficiently and effectively. In conclusion, the results of this research have drawn a basic skeleton for the development of models selection. This research also reveals much insight into the development of real-time decision support systems.