Academic literature on the topic 'Decision support systems Victoria'

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Journal articles on the topic "Decision support systems Victoria"

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Vacher, Catherine, Nicholas Ho, Adam Skinner, Jo Robinson, Louise Freebairn, Grace Yeeun Lee, Frank Iorfino, et al. "Optimizing Strategies for Improving Mental Health in Victoria, Australia during the COVID-19 Era: A System Dynamics Modelling Study." International Journal of Environmental Research and Public Health 19, no. 11 (May 26, 2022): 6470. http://dx.doi.org/10.3390/ijerph19116470.

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The ongoing COVID-19 pandemic has impacted the mental health of populations and highlighted the limitations of mental health care systems. As the trajectory of the pandemic and the economic recovery are still uncertain, decision tools are needed to help evaluate the best interventions to improve mental health outcomes. We developed a system dynamics model that captures causal relationships among population, demographics, post-secondary education, health services, COVID-19 impact, and mental health outcomes. The study was conducted in the Australian state of Victoria. The model was calibrated using historical data and was stratified by age group and by geographic remoteness. Findings demonstrate that the most effective intervention combination includes economic, social, and health sector initiatives. Assertive post-suicide attempt care is the most impactful health sector intervention, but delaying implementation reduces the potency of its impact. Some evidence-based interventions, such as population-wide community awareness campaigns, are projected to worsen mental health outcomes when implemented on their own. Systems modelling offers a powerful decision-support tool to test alternative strategies for improving mental health outcomes in the Victorian context.
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Chapman, D. F., B. R. Cullen, I. R. Johnson, and D. Beca. "Interannual variation in pasture growth rate in Australian and New Zealand dairy regions and its consequences for system management." Animal Production Science 49, no. 12 (2009): 1071. http://dx.doi.org/10.1071/an09054.

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The profitability of dairy farms in Australia and New Zealand is closely related to the amount of pasture dry matter consumed per hectare per year. There is variability in the pasture growth curve within years (seasonal variation) and between years (interannual variation) in all dairy regions in both countries. Therefore, the biological efficiency of production systems depends on the accuracy and timeliness of the many strategic and tactical decisions that influence the balance between feed supply and demand over an annual cycle. In the case of interannual variation, decisions are made with only limited quantitative information on the range of possible pasture growth outcomes. To address this limitation, we used the biophysical simulation model ‘DairyMod’ to estimate mean monthly herbage accumulation rates of annual or perennial ryegrass-based pastures in 100 years (1907–2006) for five Australian sites (Kyabram in northern Victoria, Terang in south-west Victoria, Ellinbank in Gippsland, Elliott in north-west Tasmania and Vasse in south-west Western Australia) and in 35 years (1972–2006) for three sites in New Zealand (Hamilton in the Waikato, Palmerston North in the Manawatu and Winchmore in Canterbury). The aim was to evaluate whether or not a probabilistic approach to the analysis of pasture growth could provide useful information to support decision making. For the one site where annual ryegrass was simulated, Vasse, the difference between the 25th and 75th percentile years was 20 kg DM/ha.day or less in all months when pasture growth occurred. Irrigation at Kyabram and Winchmore also resulted in a narrow range of growth rates in most months. For non-irrigated sites, the 25th–75th percentile range was narrow (10–15 kg DM/ha.day) from May or June through to September or October, because plant available soil water was adequate to support perennial ryegrass growth, and the main source of interannual variability was variation in temperature. Outside of these months, however, variability in growth was large. There was a positive relationship between total annual herbage accumulation rate and mean stocking for four southern Australian regions (northern Victoria, south-west Victoria, Gippsland and Tasmania), but there was evidence of a negative relationship between the co-efficient of variation in pasture growth and stocking rate. The latter suggests that farmers do account for risk in pasture supply in their stocking rate decisions. However, for the one New Zealand region included in this analysis, Waikato, stocking rate was much higher than would be expected based on the variability in pasture growth, indicating that farmers in this region have well defined decision rules for coping with feed deficits or surpluses. Model predictions such as those presented here are one source of information that can support farm management decision making, but should always be coupled with published data, direct experience, and other relevant information to analyse risk for individual farm businesses.
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Jalali, Ali, Phillip B. Roös, Murray Herron, Paras Sidiqui, Beau Beza, and Emma Duncan. "Modelling Coastal Development and Environmental Impacts: A Case Study Across Two Regional Towns in Australia." International Journal of Design & Nature and Ecodynamics 17, no. 4 (August 31, 2022): 491–501. http://dx.doi.org/10.18280/ijdne.170402.

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Globally there has been an increasing trend in urban growth with cities expanding rapidly, indicating a requirement for more sustainable development of cities to minimize human impacts on the environment. In Australia, urban development continues to target areas adjacent to the coastal capital and regional cities such as the Greater Geelong region in Victoria, experiencing the fastest rates of growth in the country in the last decade. This project demonstrates the ability of modelling techniques to model current and future directions in urban development across two adjacent coastal towns, Anglesea and Torquay, in Victoria. The analysis utilized Geographic Information Systems (GIS) and the CommunityViz decision support tool using a variety of assets, environmental and climatic data. The models indicated an increase in greenhouse gas emissions, energy usage and population growth, and the area was found to be highly vulnerable to the impacts of environmental changes including the potential loss of biodiversity, soil erosion, and sea level rise. The modelling approach described here can aid planners and decision makers in the future coastal urban development as well as to mitigate climate change impacts.
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Lan, Kaixin, Bohao Duan, Shichao Qiu, Yang Xiao, Meng Liu, and Haocen Dai. "Task Allocation and Traffic Route Optimization in Hybrid Fire-fighting Unmanned Aerial Vehicle Network." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 340–55. http://dx.doi.org/10.54097/hset.v9i.1864.

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With the increase of extreme weather conditions in the world, the probability of forest fires is increasing. How the forest fire management decision-making system can monitor and control the fire quickly and effectively is the key of forest fire fighting work. This paper uses SSA drones carrying high-definition and thermal imaging cameras and telemetry sensors in conjunction, as well as Repeater drones used to greatly expand the frontline low-power radio range, to support fire management decision-making systems. At the same time, explore a drone cooperation plan to deal with different fire terrains and different scales of fire conditions. The aim of this paper is to improve the existing fire management decision system in order to quickly respond to the emergency fire. Research object for the Australian state of Victoria on October 1, 2019 to January 7, 2020 wildfires, explore SSA drones and Repeater drones in the application of the forest fire, ensure that fire management decision-making system to provide the optimal number deployment scheme of fire task quickly and efficiently, and achieve the maximum efficiency and economic optimal compatibility.
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Height, Kaitlyn, Sonia Graham, Rebecca Campbell, Gina Hawkes, Silja Schrader, Louise Blessington, and Scott McKinnon. "Opportunities to Manage Herbicide Resistance through Area-Wide Management: Lessons from Australian Cropping Regions." Sustainability 14, no. 3 (February 4, 2022): 1793. http://dx.doi.org/10.3390/su14031793.

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Herbicide resistance management is often understood as a decision for individual land managers, but their decisions have far-reaching impacts for social-ecological systems. Area-wide management can reduce these impacts by supporting many land managers to cooperatively work towards a shared goal of reducing the spread of resistance. The aim of this research is to identify what support is needed for area-wide herbicide resistance management in cropping systems. Data was collected from 84 interviews with growers, public land managers and weed management advisors. Sixty-five interviews were conducted across three cropping regions of eastern Australia—Darling Downs (Queensland), Riverina (New South Wales) and Sunraysia (Victoria)—and 19 interviews were conducted with stakeholders beyond these regions. The majority (51%) of interviewees expressed concern about the spread of herbicide resistance, but only 14% described involvement in area-wide resistance management programs. Area-wide management was mostly reported to involve sharing information among stakeholders, rather than coordination or joint activities. Key barriers to participation were perceived to be the diverse agricultural industries in each region and the costs of participation. Future area-wide management program designs need to build working relationships among diverse stakeholders, clearly define the boundaries of the program and demonstrate the benefits that accrue from participation.
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Robertson, D., and Q. J. Wang. "Bayesian networks for decision analyses — an application to irrigation system selection." Australian Journal of Experimental Agriculture 44, no. 2 (2004): 145. http://dx.doi.org/10.1071/ea02178.

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Farmers are under continual pressure from Government and industry to change farm practices to meet productivity and environmental targets. In response to these pressures, farmers will make decisions to adopt practices that reflect their motivations and priorities. However, where the changes of practice are major, there may be considerable uncertainty associated with the decision-making process. Decision support tools are one method that may assist in reducing the uncertainty associated with decisions about changes in farm practices.Bayesian networks provide a useful tool to assist in the structuring and analysis of decision problems. A Bayesian network is a decision analysis framework, based on Bayesian probability theory, which allows the integration of scientific and experiential knowledge, and the uncertainty associated with this knowledge. The approach involves describing a system in terms of variables and linkages, or relationships between variables, at a level appropriate to the decision making. This is achieved through representing linkages as conditional probability tables and propagating probabilities through the network to give the likelihood of variable outcomes. Therefore, the approach ensures that treatment of risks and uncertainties is an intrinsic part of the decision-making processes. The Bayesian network is dynamic and interactive, and hence if a network previously developed does not fit a user's conceptual understanding of the system, it can be adapted quickly and simply to the cognitive understanding of the user.A case study Bayesian network has been developed for decisions associated with the selection of irrigation systems for irrigated dairy farms in Northern Victoria. This case study demonstrates that the most appropriate irrigation system for a dairy farm is dependent on factors including the amount of irrigation water available and soil types. Analysis of the Bayesian network indicates that the appropriate irrigation system is more sensitive to the income generated from pasture than to the price of water. The Bayesian network can demonstrate the impacts of decisions on the farmer's system and can allow the farmer to evaluate these impacts according to their own priorities and criteria. This information can then be used by the natural resource manager to assess the appropriate level of incentive or penalty required if the farmer is to adopt the preferred option that will also achieve preferable outcomes from a natural resource management perspective.
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Mokany, Karel, Andrew D. Moore, Phillip Graham, and Richard J. Simpson. "Optimal management of fertiliser and stocking rate in temperate grazing systems." Animal Production Science 50, no. 1 (2010): 6. http://dx.doi.org/10.1071/an09067.

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Phosphorus (P) fertilisers are one of the key tools available for increasing pasture production and the profitability of grazing enterprises. However, recent rapid changes in fertiliser price have increased the importance of developing optimal management strategies for applying P fertiliser and setting stocking rates. We applied a novel combination of process-based grazing systems modelling and randomised cash flow analyses to examine how changes in fertiliser price affect optimal fertiliser application rates and stocking rates for sheep grazing systems in south-eastern Australia, simultaneously taking into account long-term economic viability and environmental sustainability. We used ‘GrassGro’, a grazing systems decision support tool, to simulate three sheep enterprise types (Merino wethers, Merino ewes, crossbred ewes) at two locations (Hamilton, Victoria; Bookham, New South Wales). Gross margins from each year simulated in GrassGro (1966–2007) were randomised 500 times and input to a cash flow analysis that identified the financially optimal stocking rate for a range of fertiliser applications and the financial risk frontiers (combinations of stocking rate and fertiliser input for which the enterprise becomes financially unviable). For all enterprises examined at both locations, the optimal combinations of stocking rate and fertiliser application rate did not vary markedly as fertiliser price changed. Regardless of enterprise type or location, the fertiliser application rate at which the highest gross margins were achieved provided the greatest range of stocking rates that were both financially viable and environmentally sustainable. Increases in fertiliser price reduced the combinations of stocking rate and fertiliser application rate that were viable in the long term, emphasising the importance of well informed grazing management decisions.
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Biezen, Ruby, Cassandra Roberts, Kirsty Buising, Karin Thursky, Douglas Boyle, Phyllis Lau, Malcolm Clark, and Jo-Anne Manski-Nankervis. "How do general practitioners access guidelines and utilise electronic medical records to make clinical decisions on antibiotic use? Results from an Australian qualitative study." BMJ Open 9, no. 8 (August 2019): e028329. http://dx.doi.org/10.1136/bmjopen-2018-028329.

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ObjectiveThis study aimed to explore how general practitioners (GPs) access and use both guidelines and electronic medical records (EMRs) to assist in clinical decision-making when prescribing antibiotics in Australia.DesignThis is an exploratory qualitative study with thematic analysis interpreted using the Theory of Planned Behaviour (TPB) framework.SettingThis study was conducted in general practice in Victoria, Australia.ParticipantsTwenty-six GPs from five general practices were recruited to participate in five focus groups between February and April 2018.ResultsGPs expressed that current EMR systems do not provide clinical decision support to assist with antibiotic prescribing. Access and use of guidelines were variable. GPs who had more clinical experience were less likely to access guidelines than younger and less experienced GPs. Guideline use and guideline-concordant prescribing was facilitated if there was a practice culture encouraging evidence-based practice. However, a lack of access to guidelines and perceived patients’ expectation and demand for antibiotics were barriers to guideline-concordant prescribing. Furthermore, guidelines that were easy to access and navigate, free, embedded within EMRs and fit into the clinical workflow were seen as likely to enhance guideline use.ConclusionsCurrent barriers to the use of antibiotic guidelines include GPs’ experience, patient factors, practice culture, and ease of access and cost of guidelines. To reduce inappropriate antibiotic prescribing and to promote more rational use of antibiotic in the community, guidelines should be made available, accessible and easy to use, with minimal cost to practicing GPs. Integration of evidence-based antibiotic guidelines within the EMR in the form of a clinical decision support tool could optimise guideline use and increase guideline-concordant prescribing.
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Stockdale, C. R. "Levels of pasture substitution when concentrates are fed to grazing dairy cows in northern Victoria." Australian Journal of Experimental Agriculture 40, no. 7 (2000): 913. http://dx.doi.org/10.1071/ea00034.

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This paper aims to provide information for farmers and their advisers to predict levels of substitution that might be occurring under various feeding conditions in northern Victoria. The approach taken involved compiling data from research conducted in northern Victoria and subjecting these to multiple regression analysis to define the key variables affecting substitution and marginal responses in milk production when concentrates are fed. A significant relationship was obtained between level of substitution (kg DM reduction in pasture intake/kg DM of concentrates eaten) and unsupplemented pasture intake (PI, kg DM/100 kg liveweight) when concentrates are fed. The regression relationship also included species composition of the sward being grazed (species: +1 grass, 0 clover), season of the year (season: +1 spring, 0 summer, –1 autumn) and concentrate intake (kg DM/cow.day). The equation is: Substitution = –0.34 + 0.16 ( 0.035) PI + 0.16 ( 0.053) species + 0.11 ( 0.024) season+ 0.03 ( 0.014) concentrate intake [100R 2 = 50.9 (P<0.01); r.s.d. = 0.14; CV = 37.7%]. Substitution increased by 0.16 kg DM/kg DM for each increment of pasture intake. At any pasture intake, grass-dominant pastures, regardless of whether the grass was perennial ryegrass (Lolium perenne) or paspalum (Paspalum dilatatum), resulted in 0.16 kg DM/kg DM more substitution than white clover (Trifolium repens)-dominant pastures. In addition, substitution was 0.11 kg DM/kg DM higher in spring than in summer, and 0.11 kg DM/kg DM higher in summer than in autumn. Finally, substitution increased by 0.03 kg DM/kg DM for each additional kg DM of concentrates offered. Marginal returns in milk production (MR, kg extra milk/kg DM of concentrates eaten) were negatively related to substitution according to the following regression equation: MR = 2.62 – 0.80 ( 0.216) substitution – 0.28 ( 0.084) season – 0.34 ( 0.086) body condition[100R2 = 62.9 (P<0.01); r.s.d. = 0.23; CV = 29.6%]. Marginal responses were 0.28 kg/kg DM lower in spring than in summer and autumn (season: +1 spring, 0 summer–autumn), and each unit improvement in body condition reduced expected marginal returns by 0.34 kg/kg DM. These relationships, together with those developed to aid estimates of unsupplemented pasture intake, can be used as background information in decision support systems to help farmers and their advisers make more informed decisions about feeding strategies when supplements are fed than has hitherto been possible.duct
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Rozario, Venkatraman, Chu, and Abbas. "Dominant Factors for an Effective Selection System: An Australian Education Sector Perspective." Systems 7, no. 4 (November 1, 2019): 50. http://dx.doi.org/10.3390/systems7040050.

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With the latest advancements in information technologies, many organisations expect systems to provide effective support in the recruitment process and decision making. However, there is a lack of clarity on the dominant factors required for an effective recruitment system which can influence business outcomes. This paper aimed to identify the predominant factors in the employee selection process and the use of a management system for decision support. The empirical study consisted of a qualitative survey of 74 samples and a quantitative survey of 204 individual participants from 17 organisations coming from technical and further education (TAFE)/dual education (higher education and vocational education) sector members of the Victorian TAFE Association in Australia. Using commonly adopted exploratory factor analysis (EFA) of 38 variables, the data triangulation of the qualitative and quantitative analysis resulted in conformance of five dominant factors under three themes. We believe the results of the study offer actionable suggestions in developing an effective recruitment system and furthers the research in this field of study.
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Dissertations / Theses on the topic "Decision support systems Victoria"

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Alkarouri, Muhammad Abdulmuneim. "Distributed decision support systems." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555644.

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Decision support systems are a class of computer based systems that assist in some or all levels of decision making within an organisation. Recently, the growth of data captured that is useful or even critical to the successful running or conclusion of projects in science and industry has been remarkable. Thus, the development of decision support systems that are scalable in terms of the size of data processed. the number of stakeholders, and their geographical span has become of the essence. This thesis identifies the issues in developing distributed decision support systems. Building on that. an architectural style for the development of scalable and extensible software systems is introduced. Subsequently, a framework for the design of distributed decision support systems is developed. This new architectural style is the Resource Oriented Services Architecture (ROSA). It builds on Representational State Transfer (REST), an architectural style that describes the venerable design of the world wide web. An architectural design based on REST revolves around resources, representations, and hyperlinks. \Vhat it lacks is a standardised way to represent computations as resources in a scalable and extensible manner. For systems that cannot be adequately described as a web of documents, this is a shortcoming. ROSA overcomes this by defining a means of representing executable resources in a manner that is consistent with the statelessness and cacheability constraints of REST. The resulting architecture enables the scalability of the system. Additionally, desirable features such as dynamic discovery of resources and extensibility and loose coupling are attained. To illustrate this framework, two new learning algorithms are introduced and implemented as services. The first is a data structure suitable for proximity queries over large datasets of low intrinsic dimension. The other uses a random projection to carry out novelty detection over high dimensional datasets.
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Sandhu, Raghbir Singh. "Intelligent spatial decision support systems." Thesis, University College London (University of London), 1998. http://discovery.ucl.ac.uk/1317911/.

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This thesis investigates the conceptual and methodological issues for the development of Intelligent Spatial Decision Support Systems (ISDSS). These are spatial decision support systems (SDSS) integrating intelligent systems techniques (Genetic Algorithms, Neural Networks, Expert Systems, Fuzzy Logic and Nonlinear methods) with traditional modelling and statistical methods for the analysis of spatial problems. The principal aim of this work is to verify the feasibility of heterogeneous systems for spatial decision support derived from a combination of traditional numerical techniques and intelligent techniques in order to provide superior performance and functionality to that achieved through the use of traditional methods alone. This thesis is composed of four distinct sections: (i) a taxonomy covering the employment of intelligent systems techniques in specific applications of geographical information systems and SDSS; (ii) the development of a prototype ISDSS; (iii) application of the prototype ISDSS to modelling the spatiotemporal dynamics of high technology industry in the South-East of England; and (iv) the development of ISDSS architectures utilising interapplication communication techniques. Existing approaches for implementing modelling tools within SDSS and GIS generally fall into one of two schemes - loose coupling or tight coupling - both of which involve a tradeoff between generality and speed of data interchange. In addition, these schemes offer little use of distributed processing resources. A prototype ISDSS was developed in collaboration with KPMG Peat Marwick's High Technology Practice as a general purpose spatiotemporal analysis tool with particular regard to modelling high technology industry. The GeoAnalyser system furnishes the user with animation and time plotting tools for observing spatiotemporal dynamics; such tools are typically not found in existing SDSS or GIS. Furthermore, GeoAnalyser employs the client/server model of distributed computing to link the front end client application with the back end modelling component contained within the server application. GeoAnalyser demonstrates a hybrid approach to spatial problem solving - the application utilises a nonlinear model for the temporal evolution of spatial variables and a genetic algorithm for calibrating the model in order to establish a good fit for the dataset under investigation. Several novel architectures are proposed for ISDSS based on existing distributed systems technologies. These architectures are assessed in terms of user interface, data and functional integration. Implementation issues are also discussed. The research contributions of this work are four-fold: (i) it lays the foundation for ISDSS as a distinct type of system for spatial decision support by examining the user interface, performance and methodological requirements of such systems; (ii) it explores a new approach for linking modelling techniques and SDSS; (iii) it investigates the possibility of modelling high technology industry; and (iv) it details novel architectures for ISDSS based on distributed systems.
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Argile, Andrew Duncan Stuart. "Distributed processing in decision support systems." Thesis, Nottingham Trent University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259647.

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Woerlee, Auke Peter. "Decision support systems of production scheduling /." Rotterdam : Erasmus universiteit, 1991. http://catalogue.bnf.fr/ark:/12148/cb37438055v.

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Proefschrift--Rotterdam--Erasmus universiteit, 1991.
Mention parallèle de titre ou de responsabilité : Beslissingsondersteunende systemen voor korte termijn produktieplanning. Résumé en néerlandais, 4 p. Ill. par l'auteur. Bibliogr. p. 151-165. Index.
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Hodgkin, Julie. "Provision of intelligent user support in decision support systems." Thesis, University of Strathclyde, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366908.

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Kubik, Charles Paul. "Using commercial aviation information systems in operational support airlift decision support systems." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1397.

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Approved for public release; distribution is unlimited
scheduling solutions for routing aircraft, crews and logistical support needed to successfully operate in this new environment. The opportunity exists for the DoD's private aircraft operation, the Joint Operational Support Airlift Center (JOSAC), to utilize some of the same system features used in commercial operations such as NetJets to improve operations. This thesis will analyze the use of commercial air operator strategies and DSS's to be used in JOSAC to improve operational effectiveness. It will look to add new capabilities and processes used in commercial DSS's along with the implementation of the disruptive technology, microjets. Some of the potential benefits include improved operational performance, solutions to scheduling inefficiencies and improved mission readiness. With these improvements the potential for a military microjet operation in the future is a real possibility.
1st Lieutenant, United States Air Force
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Randall, Todd Andrew. "Decision support for suburban retrofitting /." *McMaster only, 2001.

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Van, Dyk Theron Van Zyl. "Decision support systems for solving discrete multicriteria decision making problems." Master's thesis, University of Cape Town, 1992. http://hdl.handle.net/11427/14300.

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Includes bibliography.
The aim of this study was the design and implementation of an interactive decision support system, assisting a single decision maker in reaching a satisfactory decision when faced by a multicriteria decision making problem. There are clearly two components involved in designing such a system, namely the concept of decision support systems (DSS) and the area of multicriteria decision making (MCDM). The multicriteria decision making environment as well as the definitions of the multicriteria decision making concepts used, are discussed in chapter 1. Chapter 2 gives a brief historical review on MCDM, highlighting the origins of some of the more well-known methods for solving MCDM problems. A detailed discussion of interactive decision making is also given. Chapter 3 is concerned with the DSS concept, including a historical review thereof, a framework for the design of a DSS, various development approaches as well as the components constituting a decision support system. In chapter 4, the possibility of integrating the two concepts, MCDM and DSS, are discussed. A detailed discussion of various methodologies for solving MCDM problems is given in chapter 5. Specific attention is given to identifying the methodologies to be implemented in the DSS. Chapter 6 can be seen as a theoretical description of the system developed, while Chapter 7 is concerned with the evaluation procedures used for testing the system. A final summary and concluding remarks are given in Chapter 8.
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Goeller, Dale Wellington. "Spreadsheet techniques for logistics decision support systems /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA271846.

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Guo, Yufeng. "Decision support systems for airline crew recovery." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976568284.

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Books on the topic "Decision support systems Victoria"

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Loucks, Daniel P., and João R. da Costa, eds. Decision Support Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76048-8.

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Moreno-Jiménez, José María, Isabelle Linden, Fatima Dargam, and Uchitha Jayawickrama, eds. Decision Support Systems X: Cognitive Decision Support Systems and Technologies. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46224-6.

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Davis, Michael W. Applied decision support. Englewood Cliffs, N.J: Prentice Hall, 1988.

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IMACS/IFORS, International Colloquium on Managerial Decision Support Systems and Knowledge Based Systems (1st 1987 Manchester England). Managerial decision support systems. Amsterdam: North-Holland, 1988.

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Introducing decision support systems. Oxford, UK: NCC Blackwell, 1994.

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Finlay, Paul N. Introducing decision support systems. Didsbury: NCC, 1998.

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Guariso, Giorgio. Environmental decision support systems. Chichester [England]: E. Horwood, 1989.

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Sànchez-Marrè, Miquel. Intelligent Decision Support Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87790-3.

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Greco, Salvatore, Vincent Mousseau, Jerzy Stefanowski, and Constantin Zopounidis, eds. Intelligent Decision Support Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96318-7.

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Berner, Eta S., ed. Clinical Decision Support Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31913-1.

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Book chapters on the topic "Decision support systems Victoria"

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Salles, Maryse. "Decision Support Systems." In Decision-Making and the Information System, 43–88. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119102663.ch2.

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Zopounidis, Constantin, and Michael Doumpos. "Decision Support Systems." In Intelligent Decision Aiding Systems Based on Multiple Criteria for Financial Engineering, 37–82. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4663-4_2.

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Crossland, Martin D. "Decision Support Systems." In Encyclopedia of GIS, 1. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_269-2.

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Walterscheid, Heinz. "Decision Support Systems." In Effektivität computergestützter Management-Entscheidungsprozesse, 21–59. Wiesbaden: Deutscher Universitätsverlag, 1996. http://dx.doi.org/10.1007/978-3-322-97723-6_3.

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Filip, Florin Gheorghe, Constantin-Bălă Zamfirescu, and Cristian Ciurea. "Decision Support Systems." In Automation, Collaboration, & E-Services, 31–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47221-8_2.

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Smith, William. "Decision Support Systems." In Systems Building with Oracle, 376–407. London: Macmillan Education UK, 2004. http://dx.doi.org/10.1007/978-0-230-00094-0_16.

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Crossland, Martin D. "Decision Support Systems." In Encyclopedia of GIS, 460. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_269.

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Crossland, Martin D. "Decision Support Systems." In Encyclopedia of GIS, 232. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_269.

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French, Simon. "Decision Support Systems." In e-Democracy, 65–82. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9045-4_5.

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Kale, Vivek. "Decision Support Systems." In Enterprise Performance Intelligence and Decision Patterns, 35–56. New York : CRC Press, [2017]: Auerbach Publications, 2017. http://dx.doi.org/10.4324/9781351228428-4.

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Conference papers on the topic "Decision support systems Victoria"

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Tatnall, Arthur, and Stephen Burgess. "Experiences in Building and Using Decision Support Systems in Postgraduate University Courses." In InSITE 2007: Informing Science + IT Education Conference. Informing Science Institute, 2007. http://dx.doi.org/10.28945/3084.

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In this paper we will relate some of our experiences in building and using simple Decision Support Systems (DSS) for use in two postgraduate subjects at Victoria University in Melbourne. These subjects are not about Decision Support Systems, but are Information Systems (IS) subjects that introduce, amongst other things, concepts of decision support and DSS. It has been our experience that the complexity, an inability to examine the logic behind decisions that have been recommended, the proprietary nature and the cost of commercial DSS make their use in teaching less than ideal. Our solution was to produce our own simple systems using Excel, Visual Basic and Visual Basic for Applications, and this has proved to be quite successful. The paper reports on building and using Decision Support Systems in this way.
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Chen, Man-kuen S., Chui-fat C. Chau, and Waldo C. Kabat. "Decision support systems." In the 1985 ACM annual conference. New York, New York, USA: ACM Press, 1985. http://dx.doi.org/10.1145/320435.320586.

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Loyko, A. O., and S. A. Gusev. "Decision Support Systems." In the 2019 10th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3345035.3345081.

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Boukhayma, Khaoula, and Abdellah ElManouar. "Evaluating decision support systems." In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2015. http://dx.doi.org/10.1109/isda.2015.7489263.

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KACPRZYK, JANUSZ. "DECISION MAKING, DECISION PROCESSES AND DECISION SUPPORT SYSTEMS." In Proceedings of the MS'10 International Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324441_0002.

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Bencsik, G., and L. Bacsardi. "Towards to decision support generalization: The Universal Decision Support System concept." In 2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES). IEEE, 2015. http://dx.doi.org/10.1109/ines.2015.7329721.

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Taktak, A. F. G., A. Eleuteri, S. P. Lake, and A. C. Fisher. "Decision support systems in cancer." In 4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20080451.

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Encheva, Sylvia, Yuriy Kondratenko, Maryna Z. Solesvik, Sharil Tumin, Theodore E. Simos, and George Psihoyios. "Decision Support Systems in Logistics." In INTERNATIONAL ELECTRONIC CONFERENCE ON COMPUTER SCIENCE. AIP, 2008. http://dx.doi.org/10.1063/1.3037065.

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Gerson, Michael, I. S. Chien, and Vasant Raval. "Computer assisted decision support systems." In the 1992 ACM SIGCPR conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/144001.144051.

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Koumboulis, Fotis N., Maria P. Tzamtzi, and Martin Pavlovic. "Decision Support Systems in Agribusiness." In 2006 IEEE International Conference on Mechatronics. IEEE, 2006. http://dx.doi.org/10.1109/icmech.2006.252570.

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Reports on the topic "Decision support systems Victoria"

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Sorensen, H. B., John S. Park, and Jr. Instructional Systems Development Decision Support. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada228052.

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Grevet, Jean-Louis M., and Alexander H. Levis. Coordination in Organizations with Decision Support Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada197951.

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Gurney, John O., Marsh Jr., Wauchope Elaine, and Kenneth. Focus of Attention in Decision Support Systems. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada294037.

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Bostick, K. V. Decision support system to select cover systems. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/10116819.

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Hawgood, John. Formal 'Systems Languages' in Decision Support Systems for Military Commanders. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada169673.

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Kennedy, John C., Piyush Sabharwall, Shannon M. Bragg-Sitton, Konor L. Frick, Patrick McClure, and D. V. Rao. Special Purpose Application Reactors: Systems Integration Decision Support. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475413.

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Hirsch, Gary B., Jack Homer, Brooke N. Chenoweth, George A. Backus, and David R. Strip. Behavior-aware decision support systems : LDRD final report. Office of Scientific and Technical Information (OSTI), November 2007. http://dx.doi.org/10.2172/934860.

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Mowrer, H. Todd, Klaus Barber, Joe Campbell, Nick Crookston, Cathy Dahms, John Day, Jim Laacke, et al. Decision support systems for ecosystem management: An evaluation of existing systems. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1997. http://dx.doi.org/10.2737/rm-gtr-296.

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Smirnov, Alexander, Tatiana Levashova, Nikolay Teslya, and Michael Pashkin. Decision Support in Socio-cyber-physical Systems: Conceptual Framework and Decision Making Stages. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2019. http://dx.doi.org/10.7546/crabs.2019.10.10.

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Riedel, Sharon L. User Acceptance and Field Implementation of Decision Support Systems. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada200412.

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