Many concrete problems may be cast in a multi-objective optimisation framework. The redundancy of existing methods for solving multi-objective programming problems susceptible to inconsistencies, coupled with the necessity for making in- herent assumptions before using a given method, make it hard for a nonspecialist to choose a method that ¯ts the situation at hand well. Moreover, using a method blindly, as suggested by the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design, development, implementation and deployment of a Decision Support System able to choose a method that is appropriate for a given problem and to apply the chosen method to solve the problem under consideration. The choice of method should be made according to the structure of the problem and the decision maker's opinion. The aim here is to embed a sample of methods representing the main multi-objective programming techniques and to help the decision maker find the most appropriate method for his problem. / Decisions Sciences / M. Sc. (Operations Research )

O presente trabalho apresenta, primeiramente, o desenvolvimento e aplicação de uma Ferramenta de Apoio à Decisão (FAD), seguindo os conceitos de Sistemas de Informação (SI), para facilitar o acesso e visualização de informações técnicas estratégicas, e que possa compor um Sistema de Apoio à Decisão (SAD) que englobe o portfólio de informações necessárias para o planejamento da priorização de obras de investimento e expansão das redes de distribuição de energia. A FAD proposta realiza o tratamento de um grupo de dados relativos a incidência de interrupções de energia da rede de distribuição de uma concessionária do setor elétrico, de forma a disponibilizar as informações depuradas para auxílio na tomada assertiva de decisão para aplicação de recursos para execução de ações de manutenção em rede de distribuição. Em seguida, exibe a inclusão de uma função de análise, no aplicativo desenvolvido como Ferramenta de Apoio à Decisão (FAD), baseada na priorização multicriterial AHP (Analytic Hierarchy Process). A aplicação do método AHP indica as estações avançadas da concessionária que possuem prioridade para a aplicação de recursos que visam à execução de ações de manutenção em rede de distribuição para a melhoria nos indicadores de continuidades do fornecimento de energia. / This paper first introduces the development and application of a Decision Support Tool (FAD), following the concepts of Information Systems (IS), to facilitate the availability and visualization of strategic techniques information, and can compose a Decision Support System (DSS) that encompasses the entire portfolio of information needed for planning the prioritization of investment works and expansion of power distribution networks. The proposed FAD performs the treatment of a group of data on the incidence of power outages in the distribution of the electric utility industry network in order to provide information to aid in purified assertive decision making for application of resources for execution maintenance actions in the distribution network. Then displays the inclusion of an analysis function, the application developed as a Tool for Decision Support (FAD), multicriteria prioritization based on AHP (Analytic Hierarchy Process). The application of AHP method indicates the advanced utility stations that have priority for use of funds aimed at the implementation of maintenance actions in the distribution network to improve the indicators of continuity of power supply.

O presente trabalho apresenta, primeiramente, o desenvolvimento e aplicação de uma Ferramenta de Apoio à Decisão (FAD), seguindo os conceitos de Sistemas de Informação (SI), para facilitar o acesso e visualização de informações técnicas estratégicas, e que possa compor um Sistema de Apoio à Decisão (SAD) que englobe o portfólio de informações necessárias para o planejamento da priorização de obras de investimento e expansão das redes de distribuição de energia. A FAD proposta realiza o tratamento de um grupo de dados relativos a incidência de interrupções de energia da rede de distribuição de uma concessionária do setor elétrico, de forma a disponibilizar as informações depuradas para auxílio na tomada assertiva de decisão para aplicação de recursos para execução de ações de manutenção em rede de distribuição. Em seguida, exibe a inclusão de uma função de análise, no aplicativo desenvolvido como Ferramenta de Apoio à Decisão (FAD), baseada na priorização multicriterial AHP (Analytic Hierarchy Process). A aplicação do método AHP indica as estações avançadas da concessionária que possuem prioridade para a aplicação de recursos que visam à execução de ações de manutenção em rede de distribuição para a melhoria nos indicadores de continuidades do fornecimento de energia. / This paper first introduces the development and application of a Decision Support Tool (FAD), following the concepts of Information Systems (IS), to facilitate the availability and visualization of strategic techniques information, and can compose a Decision Support System (DSS) that encompasses the entire portfolio of information needed for planning the prioritization of investment works and expansion of power distribution networks. The proposed FAD performs the treatment of a group of data on the incidence of power outages in the distribution of the electric utility industry network in order to provide information to aid in purified assertive decision making for application of resources for execution maintenance actions in the distribution network. Then displays the inclusion of an analysis function, the application developed as a Tool for Decision Support (FAD), multicriteria prioritization based on AHP (Analytic Hierarchy Process). The application of AHP method indicates the advanced utility stations that have priority for use of funds aimed at the implementation of maintenance actions in the distribution network to improve the indicators of continuity of power supply.

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.

Internal audit departments strive to control risk within an organization. To do this they choose specific audit areas to include in an audit plan. In order to select areas, they usually focus on those areas with the highest risk. Even though high risk areas are considered, there are various other restrictions such as resource constraints (in terms of funds, manpower and hours) that must also be considered. In some cases, management might also have special requirements. Traditionally this area selection process is conducted using manual processes and requires significant decision maker experience. This makes it difficult to take all possibilities into consideration while also catering for all resource constraints and special management requirements. In this study, mathematical techniques used in capital budgeting problems are explored to solve the IT audit area selection problem. A DSS is developed which implements some of these mathematical techniques such as a linear programming model, greedy heuristic, improved greedy heuristic and evolutionary heuristic. The DSS also implements extensions to the standard capital budgeting model to make provision for special management requirements. The performance of the mathematical techniques in the DSS is tested by applying different decision rules to each of the techniques and comparing those results. The DSS, empirical experiments and results are also presented in this research study. Results have shown that in most cases a binary 0-1 model outperformed the other techniques. Internal audit management should therefore consider this model to assist with the construction of an IT internal audit plan. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2015

Internal audit departments strive to control risk within an organization. To do this they choose specific audit areas to include in an audit plan. In order to select areas, they usually focus on those areas with the highest risk. Even though high risk areas are considered, there are various other restrictions such as resource constraints (in terms of funds, manpower and hours) that must also be considered. In some cases, management might also have special requirements. Traditionally this area selection process is conducted using manual processes and requires significant decision maker experience. This makes it difficult to take all possibilities into consideration while also catering for all resource constraints and special management requirements. In this study, mathematical techniques used in capital budgeting problems are explored to solve the IT audit area selection problem. A DSS is developed which implements some of these mathematical techniques such as a linear programming model, greedy heuristic, improved greedy heuristic and evolutionary heuristic. The DSS also implements extensions to the standard capital budgeting model to make provision for special management requirements. The performance of the mathematical techniques in the DSS is tested by applying different decision rules to each of the techniques and comparing those results. The DSS, empirical experiments and results are also presented in this research study. Results have shown that in most cases a binary 0-1 model outperformed the other techniques. Internal audit management should therefore consider this model to assist with the construction of an IT internal audit plan. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2015

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.

The aim of the thesis was to develop and evaluate a Computerised Decision Support System (CDSS) for use in pre-hospital care.The thesis was guided by a theoretical framework for developing and evaluating a complex intervention. The four studies used different designs and methods. The first study was a systematic review of randomised controlled trials. The second and the last studies had experimental and quasi-experimental designs, where the CDSS was evaluated in a simulation setting and in a clinical setting. The third study included in the thesis had a qualitative case study design.The main findings from the studies in the thesis were that there is a weak evidence base for the use of CDSS in pre-hospital care. No studies have previously evaluated the effect of CDSS in pre-hospital care. Due to the context, pre-hospital care is dependent on protocol-based care to be able to deliver safe, high-quality care. The physical format of the current paper based guidelines and protocols are the main obstacle to their use. There is a request for guidelines and protocols in an electronic format among both clinicians and leaders of the ambulance organisations. The use of CDSS in the pre-hospital setting has a positive effect on compliance with pre-hospital guidelines. The largest effect is in the primary survey and in the anamnesis of the patient. The CDSS also increases the amount of information collected in the basic pre-hospital assessment process. The evaluated CDSS had a limited effect on on-the-scene time.The developed and evaluated CDSS has the ability to increase pre-hospital patient safety by reducing the risks of cognitive bias. Standardising the assessment process, enabling explicit decision support in the form of checklists, assessment rules, differential diagnosis lists and rule out worst-case scenario strategies, reduces the risk of premature closure in the assessment of the pre-hospital patient. / För avläggande av doktorsexamen i Kvalitetsförbättring och ledarskap inom hälsa och välfärd som med tillstånd av Nämnden för utbildning och forskarutbildning vid Högskolan i Jönköping framläggs till offentlig granskning torsdagen den 5 juni 2014 kl.13.00 i sal M 204, Högskolan i Borås.

A majority of the established automotive manufacturers are under severe competitive pressure and their long term economic sustainability is threatened. In particular the transformation towards more CO2-efficient energy sources is a huge financial burden for an already investment capital intensive industry. In addition existing operations urgently need rapid improvement and even more critical is the development of highly productive, efficient and sustainable manufacturing solutions for new and updated products. Simultaneously, a number of severe drawbacks with current improvement methods for industrial production systems have been identified. In summary, variation is not considered sufficient with current analysis methods, tools used are insufficient for revealing enough knowledge to support decisions, procedures for finding optimal solutions are not considered, and information about bottlenecks is often required, but no accurate methods for the identification of bottlenecks are used in practice, because they do not normally generate any improvement actions. Current methods follow a trial-and-error pattern instead of a proactive approach. Decisions are often made directly on the basis of raw static historical data without an awareness of optimal alternatives and their effects. These issues could most likely lead to inadequate production solutions, low effectiveness, and high costs, resulting in poor competitiveness. In order to address the shortcomings of existing methods, a methodology and framework for manufacturing management decision support using simulation-based multi-objective optimisation is proposed. The framework incorporates modelling and the optimisation of production systems, costs, and sustainability. Decision support is created through the extraction of knowledge from optimised data. A novel method and algorithm for the detection of constraints and bottlenecks is proposed as part of the framework. This enables optimal improvement activities with ranking in order of importance can be sought. The new method can achieve a higher improvement rate, when applied to industrial improvement situations, compared to the well-established shifting bottleneck technique. A number of 'laboratory' experiments and real-world industrial applications have been conducted in order to explore, develop, and verify the proposed framework. The identified gaps can be addressed with the proposed methodology. By using simulation-based methods, stochastic behaviour and variability is taken into account and knowledge for the creation of decision support is gathered through post-optimality analysis. Several conflicting objectives can be considered simultaneously through the application of multi-objective optimisation, while objectives related to running cost, investments and other sustainability parameters can be included through the use of the new cost and sustainability models introduced. Experiments and tests have been undertaken and have shown that the proposed framework can assist the creation of manufacturing management decision support and that such a methodology can contribute significantly to regaining profitability when applied within the automotive industry. It can be concluded that a proof-of-concept has been rigorously established for the application of the proposed framework on real-world industrial decision-making, in a manufacturing management context.