M.Com. (Business Management) / The ultimate objective of this research report was to develop a Decision Support System, (DSS), that can be used by property professionals to enable them to make better decisions regarding property development in South Africa. The DSS addresses the problem of numerous uncertain variables in property development investment decisions. The capability of the computer to do repetitive calculations with different combinations of variables, with probabilities linked to each variable, was used in a Monte Carlo analysis. The DSS was developed on a "Lotus 1-2-3™ Release 4 for Windows" spreadsheet which makes the program adaptable to suit specific applications if the need arises. The DSSwill, however, be able to address the majority of property developments without any adaptation. The DSS was appraised by property professionals and the comments received from the respondents indicate that the primary objective stated was achieved. The DSS is best suited for property investors who are also involved in the early development phases.

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The objective of the research was to establish a scientifically-based decision making procedure for determining the amount of cash to be held at a cash point at any time without compromising the customer service level or incurring undue cost. To reach the objective, the problem was divided into the following subproblems: <ul> <li> To determine the cost parameters describing the nature of the problem of cash provision in South Africa.</li> <li> To investigate the characteristics unique to South African retail banking.</li> <li> To determine the nature of the demand distribution for a cash point.</li> <li> To develop a forecasting method appropriate for retail banking, although it was clearly stated that the methods used were specific to the branch studied.</li> <li> To investigate the existing order policies used by retail banks, as well as alternative order policies, with the aim of improving the cash replenishment process.</li> </ul> As a result of the investigation a generic decision model was developed which may be used to improve the process at branch level for retail banks in South Africa. Some suggestions were also made regarding the implementation and maintenance of the model. To investigate the cash replenishment problem, the cooperation of one of the leading retail banks in South Africa was obtained. A typical branch was selected. The total withdrawal, deposit patterns and the withdrawal patterns at the automated teller machines (ATM's) for a three month period during 1998 were investigated. The cost parameters relevant to the cash replenishment process were quantified. The approach followed was based on the classical inventory theory where the total cost of carrying inventory comprised three cost categories, i.e. storage cost, supply cost and shortage cost. Since the banks do not quantify the shortage cost, assumptions regarding the scope of the shortage cost had to be made. The next step was to determine the cost of the existing order policy followed by the branch. This figure was used as a benchmark once alternate policies were investigated. The investigation resulted in alternate policies which significantly reduced the daily cost involved in carrying inventory as well as reduced the average amount of cash carried at the branch. It was also shown, that the branch should consider using an appropriate forecasting method, since once forecasting was combined with an appropriate order policy, it was possible to reduce the cost of carrying cash inventories even further. In conclusion, the research report suggested an implementation plan to be followed at branch level pointing out that certain changes to information systems were required. In addition, training needs were identified to enable the branch operations manager to successfully use the decision support model. A comparison was drawn between the existing approach followed at the branch (which is mainly experience-based and largely of a random nature) to the proposed method. It was shown that the daily cost of carrying cash inventory could be reduced by 13 per cent per day. This represented a daily bottom line cost reduction ofR358. At the time that the research was carried out, this retail bank had 75 similar branches. Should the saving at this representative branch be extrapolated, it shows a potential saving of R8 000 000 per year at this category of branch. It was further shown that the average cash inventory at this branch could be reduced by 52 per cent using the proposed method. The study was limited to an investigation at one particular branch of a leading South African retail bank. The figures used to describe cash movements at the branch were of an extremely sensitive nature and were fairly difficult to obtain due to the way in which transactions are reported. The accuracy of the data provided by the branch could not be verified, but had to be accepted at face value. Although a particular case was investigated, a concerted effort was made to point out how the methodology may be used in the generic situation. During the period under review, the branch relocated to a complex across the street from its previous location in a busy shopping mall. This had a direct impact on the ATM withdrawal patterns at the two ATM's located at the branch. In addition, soon after the research was carried out, a number of other branches of the same retail bank were consolidated into this one particular branch. This would impact on the validity of the branch specific factors determined as part of the research. The study proved the applicability of industrial engineering principles in a service environment, where the added value of having the optimum cash amount available when required would impact directly on the bottom line of the bank and thereby enhance share-holder value. In the changing environment confronting retail banks, enhanced share-holder value is of the utmost importance to increase competitiveness and long-term survival. / Thesis (PhD (Industrial Engineering))--University of Pretoria, 2007. / Industrial and Systems Engineering / unrestricted

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This research designs a purchasing decision support system (PDSS) to assist real-world decision makings on whether to purchase or to sub-hire for equipment shortfalls problem, and to avoid shortage loss for rental business. Research methodology includes an extensive literature review on decision support systems, rental industry, and forecasting methods. A case study was conducted in a rental company to learn the real world problem and to develop the research topics. A data converter is developed to recover the missing data and transform data sets to the accumulative usage data for the forecasting model. Simulations on a number of forecasting methods was carried out to select the best method for the research data based on the lowest forecasting errors. A hybrid forecasting approach is proposed by adding company revenue data as a parameter, in addition to the selected regression model to further reduce the forecasting error. Using the forecasted equipment usage, a two stage PDSS model was constructed and integrated to the forecasting model and data converter. This research fills the gap between decision support system and rental industry. The PDSS now assists the rental company on equipments buy or hire decisions. A hybrid forecasting method has been introduced to improve the forecasting accuracy significantly. A dada converter is designed to efficiently resolve data missing and data format problems, which is very common in real world.

Encounters in which shared decision-making occur relies on patients and healthcare professionals establishing a partnership. Yet, little is known about the therapeutic relationship (TR) specifically for the implementation and use of patient decision aids (PDA) to facilitate shared decision-making. The aim of this thesis was to explore how the TR is considered when planning PDA implementation for patients eligible for or with an implantable cardioverter-defibrillator (ICD). Using Thorne's interpretive description approach, I conducted a secondary thematic analysis using transcripts with 17 healthcare professionals, ten patients and three family members. Findings were mapped to the TR elements. I identified three themes. First, Pieces of the puzzle: Elements of the TR revealed that while respect and therapeutic communication were identified as important for PDA implementation, other TR were either referred to implicitly or not at all. Second, Good intentions and challenges of establishing a TR revealed that healthcare professionals wanted to engage in TR but lacked time and felt discomfort navigating ICD decisions. Finally, in PDA as support for the TR, participants considered PDAs as being able to facilitate TR elements such as communication and respect, enhancing the consultation. In conclusion, there is a role for TR elements when planning PDA implementation. Further research is needed to explore the other therapeutic relationship elements of genuineness, manifesting a presence, active listening, and reciprocity.

This study develops a decision-support framework assisting the design of non-residential buildings with natural ventilation. The framework is composed of decision modules with input, analysis algorithms and output of natural ventilation design. The framework covers ventilation with natural driving force and mechanical-assisted ventilation. The framework has two major assessment levels: feasibility assessment and comparison of alternative natural ventilation approaches. The feasibility assessment modules assess the potential of the site with the design proposition for natural ventilation in terms of wind, temperature, humidity, noise and pollution conditions. All of the possible natural ventilation approaches and system designs are assessed by first applying constraints functions to each of the alternatives. Then the comparison of alternative approaches to natural ventilation continues by assessing the critical performance mandates that include energy savings, thermal comfort, acoustic control, indoor air quality and cost. Approaches are finally ranked based on their performance. / Ph. D.

Public Health requires a trans-disciplinary approach to tackle the breadth and depth of the issues it faces. Public health decisions are reached through the compilation of multiple data sources and their thoughtful synthesis. The complexity and importance of these decisions necessitates a variety of approaches, with simulations increasingly being relied upon. This dissertation describes several research efforts that demonstrate the utility of highly detailed simulations in public health decision-making. Simulations are frequently used to represent dynamic processes and to synthesize data to predict future outcomes, which can be used in cost-benefit and course of action analyses. The threat of pandemic influenza and its subsequent arrival prompted many simulation-based studies. This dissertation details several such studies conducted at the federal policy level. Their use for planning and the rapid response to the unfolding crisis demonstrates the integration of highly detailed simulations into the public health decision-making process. Most analytic methods developed by public health practitioners rely on historical data sources, but are intended to be broadly applicable. Oftentimes this data is limited or incomplete. This dissertation describes the use of highly detailed simulations to evaluate the performance of outbreak detection algorithms. By creating methods that generate realistic and configurable synthetic data, the reliance on these historical samples can be reduced, thus facilitating the development and improvement of methods for public health practice. The process of decision-making itself can significantly influence the decisions reached. Many fields use simulations to train and evaluate, however, public health has yet to fully adopt these approaches. This dissertation details the construction of highly detailed synthetic data that was used to build an interactive environment designed to evaluate the decision-making processes for pertussis control. The realistic data sets provide sufficient face validity to experienced public health practitioners, creating a natural and effective medium for training and evaluation purposes. Advances in high-performance computing, information sciences, computer science, and epidemiology are enabling increasing innovation in the application of simulations. This dissertation illustrates several applications of simulations to relevant public health practices and strongly argues that highly detailed simulations have an essential role to play in Public Health decision-making. / Ph. D.

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

The ubiquity of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to their widespread adoption across diverse domains like education, self-driving cars, healthcare, and more. AI is making its way into the industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation, driven by challenges such as water demand forecasting, wastewater treatment, asset maintenance and management, and water quality assessment. Water utilities are at different stages in their journey of digital transformation, and its decision-makers, who are non-expert stakeholders in AI applications, must understand the technology to make informed decisions. The non-expert stakeholders should know that while AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration, and ethical concerns that should be considered before implementing it for real-world applications. Civil engineering is a licensed profession where critical decision-making is involved. Failure of critical decisions by civil engineers may put their license at risk, and therefore trust in any decision-support technology is crucial for its acceptance in real-world applications. This research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) to facilitate the successful application of AI in the water sector. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities are at an early stage of implementing AI as they face concerns regarding the blackbox nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation. / Master of Science / The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) in various industries like education, self-driving cars, healthcare, and more has spurred interest in its potential application in the water sector. As the water sector undergoes a digital transformation to address challenges such as water demand forecasting, wastewater treatment, asset management, and water quality assessment, water utilities need to understand the benefits and challenges of AI technology. Automating water sector operations through AI involves high risk as it has a huge ecological, economic, and sociological impact on society. Water utilities are non-expert end users of AI and they should be aware of its challenges such as data management, model development, domain knowledge integration, and ethical concerns when implementing AI for real-world applications. To address these challenges, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) to help water utilities successfully apply AI technology in their system. We conduct pilot interviews and surveys with small, medium, and large water utilities across the United States to capture their current AI practices and challenges. The research results led us to find that water utilities are still at an early stage of adopting AI in their system and are faced with issues such as blackbox nature of the technology, its trustworthiness for real-world application, and sustainability at the utilities. We believe that aiWATERS will serve as a relevant guide for water utilities and will help them overcome current AI-based challenges.