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.

Water distribution system pipes span thousands of miles and form a significant part of the total infrastructure of the country. Rehabilitation of this underground infrastructure is one of the biggest challenges currently facing the water industry. Water main deterioration is twofold: the main itself loses strength over time and breaks; also, there is degradation of water quality and hydraulic capacity due to build of material within a main. The increasing repair and damage costs and degrading services demand that a deteriorating water main be replaced at an optimal time instead of continuing to repair it. In addition, expanding business districts, indirect costs, and interruptions including protected areas, waterways and roadways require examination of trenchless technologies for pipe installation. In this thesis a new threshold break rate criterion for the optimal replacement of pipes is provided. As opposed to the traditional present worth cost (PWC) criterion, the derived method uses the equivalent uniform annualized cost (EUAC). It is shown the EUAC based threshold break rate subsumes the PWC based threshold break rate. In addition, practicing engineers need a user-friendly decision support system to aid in the optimal pipeline replacement process. They also need a task-by-task cost evaluation in a project. As a part of this thesis a comprehensive decision support system that includes both technology selection knowledge base and cost evaluation spreadsheet program within a graphical user interface framework is developed. Numerical examples illustrating the theoretical derivations are also included. / Master of Science

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.

The paradigm of Decision Support Systems (DSS) is to support decision-making, while an Expert System’s (ES) major objective is to provide expert advice in specialised situations. Knowledge-Based DSS (KB-DSS), also called Intelligent Decision Support Systems (IDSS), integrate traditional DSS with the advances of ES. A KB-DSS’ knowledge base usually contains knowledge expressed by an expert and captured by a knowledge engineer. The indirect transfer between the domain expert and the knowledge base through a knowledge engineer may lead to a long and inefficient knowledge acquisition process. This thesis compares 11 DSS packages in search of a (KB-) DSS generator where domain experts can specify and maintain a Specific Decision Support System (SDSS) to assist users in making decisions. The proposed (KB-) DSS-generator is tested with a university and study-program prototype. Since course and study plan programs change intermittently, the (KB-) DSS’ knowledge base enables domain experts to set and maintain their course and study plan rules without the assistance of a knowledge engineer. Criteria are set to govern the (KB-) DSS generator search process. Example knowledge base rules are inspected to determine if domain experts will be able to maintain a set of production rules used in a student registration advice system. By developing a prototype and inspecting knowledge base rules, it was found that domain experts would be able to maintain their knowledge in the decentralised knowledge base, on condition that the objects and attributes used in the rule base were first specified by a builder/programmer. / Dissertation (MSc Computer Science)--University of Pretoria, 2005. / Computer Science / unrestricted

Syftet med denna studie är att utreda begreppen big data och analytics. Utifrån vetenskapliga teorier om begreppen undersöks hur konsultföretag uppfattar och använder sig av big data och analytics. För att skapa en nyanserad bild har även en organisation inom vården undersökts för att få kunskap om hur de kan dra nytta av big data och analytics. Ett antal viktiga svårigheter och framgångsfaktorer kopplade till båda begreppen presenteras. De svårigheterna kopplas sedan ihop med en framgångsfaktor som anses kunna bidra till att lösa det problemet. De mest relevanta framgångsfaktorer som identifierats är att högkvalitativ data finns tillgänglig men även kunskap och kompetens kring hur man hanterar data. Slutligen tydliggörs begreppens innebörd där man kan se att big data oftast beskrivs ur dimensionerna volym, variation och hastighet och att analytics i de flesta fall syftar till att deskriptiv och preventiv analys genomförs. / The purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.

A strong trend of returning to nuclear power is evident in different places in the world. Forty-five countries are planning to add nuclear power to their grids and more than 66 nuclear power plants are under construction. Nuclear power plants that generate electricity and steam need to improve safety to become more acceptable to governments and the public. One novel practical solution to increase nuclear power plants' safety factor is to build them away from urban areas, such as offshore or underground. To date, Land-Based siting is the dominant option for siting all commercial operational nuclear power plants. However, the literature reveals several options for building nuclear power plants in safer sitings than Land-Based sitings. The alternatives are several and each has advantages and disadvantages, and it is difficult to distinguish among them and choose the best for a specific project. In this research, we recall the old idea of using the alternatives of offshore and underground sitings for new nuclear power plants and propose a tool to help in choosing the best siting technology. This research involved the development of a decision model for evaluating several potential nuclear power plant siting technologies, both those that are currently available and future ones. The decision model was developed based on the Hierarchical Decision Modeling (HDM) methodology. The model considers five major dimensions, social, technical, economic, environmental, and political (STEEP), and their related criteria and sub-criteria. The model was designed and developed by the author, and its elements' validation and evaluation were done by a large number of experts in the field of nuclear energy. The decision model was applied in evaluating five potential siting technologies and ranked the Natural Island as the best in comparison to Land-Based, Floating Plant, Artificial Island, and Semi-Embedded plant.

Master of Regional and Community Planning / Department of Landscape Architecture/Regional and Community Planning / Claude A. Keithley / Planning Support Systems allow planners to create alternative development scenarios to forecast a more accurate and precise future trend of development in their communities. The software What If?™ has been developed and introduced in the planning profession since its first release in the 1990’s. This report evaluates the software What If?™ based on the planning decision-making process. The report provides three aspects of evaluation: technical, empirical, and subjective. In addition, the paper will be also providing an overall understanding of the analytical capability of What If?™, and an overview of its operating procedures.

This research compared the use of an electronic meeting system tool to a manual group process in large and small groups in a controlled laboratory experiment. Outcomes measured include the quality of decision, the time taken in various stages of the decision making process, and group member satisfaction. A research model of the variables influencing group decision making was developed. The six independent variables included in this model are group size, the rule by which the group makes a decision, the incentives driving the group, the distribution of useful information within the group, the task complexity, and the meeting support (electronic or manual). In this research group size and method of support were manipulated, while the other variables were controlled. A decision-making task was developed for this research to specify and manipulate the six independent variables. The task described a product mix problem in which information on each product was given to group members. The group shared information and jointly determined an outcome. The group used an unanimous decision rule to choose a solution. A numerical outcome was used to objectively measure decision quality. Each member of the group received a cash payoff determined by the group's solution as incentive in accomplishing the task. All groups found the optimal solution. The simplicity of the task may have minimized the differences found between groups. There was no significant difference in general member satisfaction or time to decision. Prior knowledge was found to influence general member satisfaction and the time needed for the group to share information. Members of large groups perceived more uneven distribution of participation than members of small groups. Voting differences were very large: large groups took significantly more votes than small groups, and electronic groups took significantly more votes than manual groups. "Conjunctive" and "disjunctive" task descriptions are used to discuss task/tool interaction.