An index of regional sustainability : a GIS-based multiple criteria analysis decision support system for progressing sustainability

GIS (Geographical Information Systems) based decision support tools will be useful in helping guide regions to sustainability. These tools need to be simple but effective at identifying, for regional managers, areas most in need of initiatives to progress sustainability. Multiple criteria analysis (MCA) has been used as a decision support tool for a wide number of applications, as it provides a systematic framework for evaluating various options. It has the potential to be used as a tool for sustainability assessment, because it can bring together the sustainability criteria from all pillars, social, economic and environmental, to give an integrated assessment of sustainability. Furthermore, the use of GIS and MCA together is an emerging addition to conducting sustainability assessments. This paper further develops a sustainability assessment framework developed for the Glenelg Hopkins Catchment Management Authority region of Victoria, Australia by providing a GIS-based decision support system for regional agencies. This tool uses multiple criteria analysis in a GIS framework to assess the sustainability of sub-catchments in the Glenelg Hopkins Catchment. The multiple criteria analysis based on economic, social and environmental indicators developed in previous stages of this project was used as the basis to build a model in ArcGIS1. The GIS-based multiple criteria analysis, called An Index of Regional Sustainability Spatial Decision Support System (AIRS SDSS),
produced maps showing sub-catchment sustainability, and environmental, social and economic condition. As a result, this tool is able to highlight those sub-catchments most in need of assistance with achieving sustainability. It will also be a valuable tool for evaluation and monitoring of strategies for sustainability. This paper shows the usefulness of GIS-based multiple criteria analysis to enhance the monitoring and evaluation of sustainability at the regional to sub-catchment scale.

A fuzzy decision support method for customer preferences analysis based on Choquet integral

The explosion of the Web 2:0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.

Application of an Adaptive Neural Network to Medical Decision Support

Medical diagnostic and prognostic problems are prime examples of decision making in the face of uncertainty. In this paper, we investigate the applicability of the Fuzzy ARTMAP neural network as an intelligent decision support system in clinical medicine. In particular, Fuzzy ARTMAP is employed as a predictive model for prognosis of complications in patients admitted to the Coronary Care Units. A number of off-line and on-line experiments have been conducted with various network parameter settings, training methods, and learning rules. The results are compared with those from other systems including the logistic regression model. In addition, the outcomes of a set of on-line learning experiments revealed the potential of employing Fuzzy ARTMAP as an autono-mously learning system that is able to learn perpetually and, at the same time, to provide useful decision support.

Resolving future fire management conflicts using multicriteria decision making

Management strategies to reduce the risks to human life and property from wildfire commonly involve burning native vegetation. However, planned burning can conflict with other societal objectives such as human health and biodiversity conservation. These conflicts are likely to intensify as fire regimes change under future climates and as growing human populations encroach farther into fire-prone ecosystems. Decisions about managing fire risks are therefore complex and warrant more sophisticated approaches than are typically used. We applied a multicriteria decision making approach (MCDA) with the potential to improve fire management outcomes to the case of a highly populated, biodiverse, and flammable wildland-urban interface. We considered the effects of 22 planned burning options on 8 objectives: house protection, maximizing water quality, minimizing carbon emissions and impacts on human health, and minimizing declines of 5 distinct species types. The MCDA identified a small number of management options (burning forest adjacent to houses) that performed well for most objectives, but not for one species type (arboreal mammal) or for water quality. Although MCDA made the conflict between objectives explicit, resolution of the problem depended on the weighting assigned to each objective. Additive weighting of criteria traded off the arboreal mammal and water quality objectives for other objectives. Multiplicative weighting identified scenarios that avoided poor outcomes for any objective, which is important for avoiding potentially irreversible biodiversity losses. To distinguish reliably among management options, future work should focus on reducing uncertainty in outcomes across a range of objectives. Considering management actions that have more predictable outcomes than landscape fuel management will be important. We found that, where data were adequate, an MCDA can support decision making in the complex and often conflicted area of fire management.

Mixed-initiative decision support for care planning based on clinical practice guidelines

Care Plan On-Line (CPOL) is an intranet based system that supports a “Coordinated Care” model for chronic/complex disease management. CPOL combines provision of solicited and unsolicited advice features based on integration of the electronic medical record (EMR) with its decision support logic. The objective is to support General Practitioners (GPs) in formulating a 12-month care plan of services such that: (a) the plan is proactive and patient-centered; (b) the GP is kept in awareness of project- and diseasespecific clinical practice guidelines; and (c) the support integrates with GP workflow in a natural fashion. A key feature of our approach is to blur the distinction of EMR and decision support by presenting guidelines in layers with the top-most being a problem-oriented presentation of patient status, progressing on through to patient-independent supporting evidence. In conjunction with a degree of automated inclusion of care planning services, the system demonstrates mixed user and software initiative. We describe the CPOL deployment setting, the challenges of guideline-based clinical decision support, our approach to guideline delivery, and the CPOL architecture.

A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

Exploring creativity support systems for the NE"X"T generation

This paper explores the implementation of a creativity support system for tertiary students studying games design and development at Deakin University, Victoria, Australia. The students at the centre of this study are the ‘next’ generation of learners and are often called the net generation because of their pre-imposed affinity for all things ‘online’. The creativity support system for the games students is designed within a ‘whole’ systems context. Focusing on only one tool to augment a person’s creativity does not take into consideration social factors that are pertinent on a person ability to grow their creative behaviours. This study will present a set of factors that each creativity support system should employ to facilitate creative abilities within people, with particular focus on how social activities help to enhance creativity.

The development of a mobile decision support system

The emergence of mobile computing environments brings out various changes in the requirements and applications involving distributed data and has made the traditional Intelligent Decision Support System (IDSS) architectures based on the client/server model ineffective in mobile computing environments. This paper discusses the deficiencies of the current IDSS architectures based on data warehouse, on-line analysis processing (OLAP), model base (MB) and knowledge based (KB) technologies. By adopting the agent technology, the paper extends the IDSS system architecture to the Mobile Decision Support System (MDSS) architecture. The logical structure and the application architecture of the MDSS and the mechanisms and implementation strategies of the User Access Agent System, a major component of the MDSS, are described in this paper.

Exploring creativity support systems for the Ne"X"t generation

This paper explores the implementation of a creativity support system (CSS) for tertiary students studying Games Design and Development at Deakin University, Victoria, Australia. The students at the centre of this study are the ‘next’ generation of learners and are often called the Internet generation because of their pre-imposed for ‘online’ and being ‘connected’. The CSS for the games students is designed within a context that encompasses a ‘whole’ system, as focusing on only one component to augment a person’s creativity does not take into consideration the multitude of factors, for example social factors, that are pertinent on a person ability to grow their creative behaviours. This study will present a set of factors that each CSS should employ to facilitate creative abilities within people, with particular focus on how social activities help to enhance creativity.

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents' track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified.

Neurofuzzy decision support system for efficient risk allocation in public-private partnership infrastructure projects

The performance of public-private partnership (PPP) infrastructure projects is largely contingent on whether the adopted risk allocation (RA) strategy is efficient. Theoretical frameworks drawing on the transaction cost economics and the resource-based view of organizational capability are able to explain the underlying mechanism but unable to accurately forecast efficient RA strategies. In this paper, a neurofuzzy decision support system (NFDSS) was developed to assist in the RA decision-making process in PPP projects. By combining fuzzy and neural network techniques, a synthesized fuzzy inference system was established and taken as the core component of the NFDSS. Evaluation results show that the NFDSS can forecast efficient RA strategies for PPP infrastructure projects at a highly accurate and effective level. A real PPP infrastructure project is used to demonstrate the NFDSS and its practical significance.