Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling salesman problem, the vehicle routing problem and the job shop scheduling problem. The paper introduces an Ant Colony Optimization approach to solve the problem of determining a job-sequence that minimizes the overall tardiness for a given set of jobs to be processed on a single, continuously available machine, the Single Machine Total Tardiness Problem. We experiment with various heuristic information as well as with variants for local search. Experiments with 250 benchmark problems with 50 and 100 jobs illustrate that Ant Colony Optimization is an adequate method to tackle the SMTTP. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

No-wait scheduling problem refers to the set of problems in which a number of jobs are available for processing on a number of machines with the added constraint that there should be no waiting time between consecutive operations of the jobs. It is well-known that most of the no-wait scheduling problems are strongly NP-hard. Moreover, no-wait scheduling problems have numerous real-life applications. This thesis studies a wide range of no-wait scheduling problems, along with side constraints that make such problems more applicable. First, 2-machine no-wait flow shop problem is studied. Afterwards, setup times and single server constraints are added to this problem in order to make it more applicable. Then, job shop version of this problem is further researched. Analytical results for both of these problems are presented; moreover, efficient algorithms are developed and applied to large instances of these problems. Afterward, general no-wait flow shop problem (NWFS) is the focus of the thesis. First, the NWFS is studied; mathematical models as well as metaheuristics are developed for NWFS. Then, setup times are added to NWFS in order to make the problem more applicable. Finally, the case of sequence dependent setup times is further researched. Efficient algorithms are developed for both problems. Finally, no-wait job shop (NWJS) problem is studied. Literature has proposed different methods to solve NWJS; the most successful approaches decompose the problem into a timetabling sub-problem and a sequencing sub-problem. Different sequencing and timetabling algorithms are developed to solve NWJS. This thesis provides insight to several no-wait scheduling problems. A number of theorems are discussed and proved in order to find the optimum solution of no-wait problems with special characteristics. For the problems without such characteristics, mathematical models are developed. Metaheuristics are utilized to deal with large-instances of NP-hard problems. Computational results show that the developed methods in this thesis are very effective and efficient compared to the competitive methods available in the literature.

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This study sets out to explore and describe the life experience of spirituality within the context of daily life. What is that through daily interaction of their lives, persons interviewed as part of this research experienced as a sense of meaning and wholeness, and which these persons identify and value as contributing to their sense of living spiritually? Thus daily life informed by these kinds of so-called spiritual engagements and practices may provide a way of discovering or recovering the sacred in ordinary situations and activities of their lives. Such engagements and practices may help to strengthen a consciousness that nurtures a spirituality of human development. / Spirtuality in this sense refers to the practices and learning through which individuals and societies attempt to move beyond self-centred positions of power and control, to share experiences that articulate care of the self, and of seeking meaning, wisdom, virtue, joy and harmony in, and with life. To explore the significant human experience of spirituality in daily life, the heuristic methodology developed by Clark Moustakas (1990) was employed. This approach utilizes the concepts and processes of identifying with the focus of the inquiry, self dialogue, tacit knowing, intuition, indwelling, focusing, and the internal frame of reference. The six phases of heuristic research are initial engagement, immersion, incubation, illumination, explication, and creative synthesis. Seventeen participants (co-researchers) were selected from a small group of Western Australian volunteers who claimed to have experienced the topic of investigation. They were interviewed informally and conversationally with the open- ended question, “Describe as fully as possible as possible your experience of spirituality in daily life”. These interviews were analysed, yielding eleven extended descriptions of the experience, three of which make up the individual depictions, a composite depiction of the experience, two exemplary portraits of the experience, and a creative synthesis of the experience. / Thesis (PhD)--University of South Australia, 2004.

The ever increasing use of computer systems in society brings emergent challenges to companies and system designers. The reliability of software and hardware can be financially critical, and lives can depend on it. The growth in size and complexity of software, and increasing concurrency, compounds the problem. The potential for errors is greater than ever before, and the stakes are higher than ever before. Formal methods, particularly model checking, is an approach that attempts to prove mathematically that a model of the behaviour of a product is correct with respect to certain properties. Certain errors can therefore be proven never to occur in the model. This approach has tremendous potential in system development to provide guarantees of correctness. Unfortunately, in practice, model checking cannot handle the enormous sizes of the models of real-world systems. The reason is that the approach requires an exhaustive search of the model to be conducted. While there are exceptions, in general model checkers are said not to scale well. In this thesis, we deal with this scaling issue by using a guiding technique that avoids searching areas of the model, which are unlikely to contain errors. This technique is based on a process of model abstraction in which a new, much smaller model is generated that retains certain important model information but discards the rest. This new model is called a heuristic. While model checking using a heuristic as a guide can be extremely effective, in the worst case (when the guide is of no help), it performs the same as exhaustive search, and hence it also does not scale well in all cases. A second technique is employed to deal with the scaling issue. This technique is based on the concept of random walks. A random walk is simply a `walk' through the model of the system, carried out by selecting states in the model randomly. Such a walk may encounter an error, or it may not. It is a non-exhaustive technique in the sense that only a manageable number of walks are carried out before the search is terminated. This technique cannot replace the conventional model checking as it can never guarantee the correctness of a model. It can however, be a very useful debugging tool because it scales well. From this point of view, it relieves the system designer from the difficult task of dealing with the problem of size in model checking. Using random walks, the effort goes instead into looking for errors. The effectiveness of model checking can be greatly enhanced if the above two techniques are combined: a random walk is used to search for errors, but the walk is guided by a heuristic. This in a nutshell is the focus of this work. We should emphasise that the random walk approach uses the same formal model as model checking. Furthermore, the same heuristic technique is used to guide the random walk as a guided model checker. Together, guidance and random walks are shown in this work to result in vastly improved performance over conventional model checking. Verification has been sacrificed of course, but the new technique is able to find errors far more quickly, and deal with much larger models.

The ever increasing use of computer systems in society brings emergent challenges to companies and system designers. The reliability of software and hardware can be financially critical, and lives can depend on it. The growth in size and complexity of software, and increasing concurrency, compounds the problem. The potential for errors is greater than ever before, and the stakes are higher than ever before. Formal methods, particularly model checking, is an approach that attempts to prove mathematically that a model of the behaviour of a product is correct with respect to certain properties. Certain errors can therefore be proven never to occur in the model. This approach has tremendous potential in system development to provide guarantees of correctness. Unfortunately, in practice, model checking cannot handle the enormous sizes of the models of real-world systems. The reason is that the approach requires an exhaustive search of the model to be conducted. While there are exceptions, in general model checkers are said not to scale well. In this thesis, we deal with this scaling issue by using a guiding technique that avoids searching areas of the model, which are unlikely to contain errors. This technique is based on a process of model abstraction in which a new, much smaller model is generated that retains certain important model information but discards the rest. This new model is called a heuristic. While model checking using a heuristic as a guide can be extremely effective, in the worst case (when the guide is of no help), it performs the same as exhaustive search, and hence it also does not scale well in all cases. A second technique is employed to deal with the scaling issue. This technique is based on the concept of random walks. A random walk is simply a `walk' through the model of the system, carried out by selecting states in the model randomly. Such a walk may encounter an error, or it may not. It is a non-exhaustive technique in the sense that only a manageable number of walks are carried out before the search is terminated. This technique cannot replace the conventional model checking as it can never guarantee the correctness of a model. It can however, be a very useful debugging tool because it scales well. From this point of view, it relieves the system designer from the difficult task of dealing with the problem of size in model checking. Using random walks, the effort goes instead into looking for errors. The effectiveness of model checking can be greatly enhanced if the above two techniques are combined: a random walk is used to search for errors, but the walk is guided by a heuristic. This in a nutshell is the focus of this work. We should emphasise that the random walk approach uses the same formal model as model checking. Furthermore, the same heuristic technique is used to guide the random walk as a guided model checker. Together, guidance and random walks are shown in this work to result in vastly improved performance over conventional model checking. Verification has been sacrificed of course, but the new technique is able to find errors far more quickly, and deal with much larger models.

The ever increasing use of computer systems in society brings emergent challenges to companies and system designers. The reliability of software and hardware can be financially critical, and lives can depend on it. The growth in size and complexity of software, and increasing concurrency, compounds the problem. The potential for errors is greater than ever before, and the stakes are higher than ever before. Formal methods, particularly model checking, is an approach that attempts to prove mathematically that a model of the behaviour of a product is correct with respect to certain properties. Certain errors can therefore be proven never to occur in the model. This approach has tremendous potential in system development to provide guarantees of correctness. Unfortunately, in practice, model checking cannot handle the enormous sizes of the models of real-world systems. The reason is that the approach requires an exhaustive search of the model to be conducted. While there are exceptions, in general model checkers are said not to scale well. In this thesis, we deal with this scaling issue by using a guiding technique that avoids searching areas of the model, which are unlikely to contain errors. This technique is based on a process of model abstraction in which a new, much smaller model is generated that retains certain important model information but discards the rest. This new model is called a heuristic. While model checking using a heuristic as a guide can be extremely effective, in the worst case (when the guide is of no help), it performs the same as exhaustive search, and hence it also does not scale well in all cases. A second technique is employed to deal with the scaling issue. This technique is based on the concept of random walks. A random walk is simply a `walk' through the model of the system, carried out by selecting states in the model randomly. Such a walk may encounter an error, or it may not. It is a non-exhaustive technique in the sense that only a manageable number of walks are carried out before the search is terminated. This technique cannot replace the conventional model checking as it can never guarantee the correctness of a model. It can however, be a very useful debugging tool because it scales well. From this point of view, it relieves the system designer from the difficult task of dealing with the problem of size in model checking. Using random walks, the effort goes instead into looking for errors. The effectiveness of model checking can be greatly enhanced if the above two techniques are combined: a random walk is used to search for errors, but the walk is guided by a heuristic. This in a nutshell is the focus of this work. We should emphasise that the random walk approach uses the same formal model as model checking. Furthermore, the same heuristic technique is used to guide the random walk as a guided model checker. Together, guidance and random walks are shown in this work to result in vastly improved performance over conventional model checking. Verification has been sacrificed of course, but the new technique is able to find errors far more quickly, and deal with much larger models.

This project questions and examines the impact of 'take away' culture on our society's lifestyle. The research considers the term 'take away' in relation to food and to broader behaviours such as models of social conduct or lifestyle related to consumerism.The thesis embodies the creative exploration into the relationship between these areas and the outcome is an animated cyclic narrative that illustrates and reflects the concept of 'take away'.The project is constituted as practice-based research. Seventy percent of the final assessment will be associated with the practical work and thirty percent with the contextualising exegesis.

This is a visual arts project consisting of 20% exegesis and 80% practical work. My work explores the visual possibilities of using the digital accumulation of data to convey socio-political concepts in relation to the surveillance of the individual in modern western society. The nature of surveillance is investigated with reference to Michel Foucault's metaphorical use of Jeremy Bentham's panopticon in describing the organization of society in the modern nation state. My critical interest lies in the intrusive aspect of surveillance in regard to the privacy of the individual and the concomitant sense of alienation and disempowerment. The concept of 'abstract reality' has been developed to describe the nature of the surveillance of the individual in the modern nation state.

This project explores the basic tenets of abstract expressionism and is considered in relation to the idea of the sublime, limitlessness and the formless. In this research I am interested in investigating the progression from two-dimensional non-representational painting, through experimentation with light mediating materials to projection of the painting via the medium of film. Light is used to intensify the image with a view to expand the viewer’s awareness and understanding of the sublime. The research seeks to find ways that allow the viewer to explore the feeling of uncertainty and the sensation of wonderment. Through an ephemeral spaciousness that has no boundaries, the spectator is encouraged through contemplation to transform their experiences of the finite in order to approach the infinite and the sublime.