Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.

Decision support system for production control: Multiple criteria decision making in practice

This paper presents a development of decision support systems for solving scheduling problems. It consists of two parts — the first describing the production processes which can be handled by the system and the second describing how the system works.

Towards a model for exploring the relationship between managerial decision problems and decision support opportunities

The organisational decision making environment is complex, and decision makers must deal with uncertainty and ambiguity on a continuous basis. Managing and handling decision problems and implementing a solution, requires an understanding of the complexity of the decision domain to the point where the problem and its complexity, as well as the requirements for supporting decision makers, can be described. Research in the Decision Support Systems domain has been extensive over the last thirty years with an emphasis on the development of further technology and better applications on the one hand, and on the other hand, a social approach focusing on understanding what decision making is about and how developers and users should interact. This research project considers a combined approach that endeavours to understand the thinking behind managers’ decision making, as well as their informational and decisional guidance and decision support requirements. This research utilises a cognitive framework, developed in 1985 by Humphreys and Berkeley that juxtaposes the mental processes and ideas of decision problem definition and problem solution that are developed in tandem through cognitive refinement of the problem, based on the analysis and judgement of the decision maker. The framework facilitates the separation of what is essentially a continuous process, into five distinct levels of abstraction of manager’s thinking, and suggests a structure for the underlying cognitive activities. Alter (2004) argues that decision support provides a richer basis than decision support systems, in both practice and research. The constituent literature on decision support, especially in regard to modern high profile systems, including Business Intelligence and Business analytics, can give the impression that all ‘smart’ organisations utilise decision support and data analytics capabilities for all of their key decision making activities. However this empirical investigation indicates a very different reality.

Municipal solid waste management system: decision support through systems analysis

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

A decision support process to compare Riparian revegetation options in Scheu Creek catchment in North Queensland

While riparian vegetation can play a major role in protecting land, water and natural habitat in catchments, there are high costs associated with tree planting and establishment and in diverting land from cropping. The distribution of costs and benefits of riparian revegetation creates conflicts in the objectives of various stakeholder groups. Multicriteria analysis provides an appropriate tool to evaluate alternative riparian revegetation options, and to accommodate the conflicting views of various stakeholder groups. This paper discusses an application of multicriteria analysis in an evaluation of riparian revegetation policy options for Scheu Creek, a small sub-catchment in the Johnstone River catchment in north Queensland, Australia. Clear differences are found in the rankings of revegetation options for different stakeholder groups with respect to environmental, social and economic impacts. Implementation of a revegetation option will involve considerable cost for landholders for the benefits of society. Queensland legislation does not provide a means to require farmers to implement riparian revegetation, hence the need for subsidies, tau incentives and moral suasion. (C) 2001 Academic Press.

A decision support system for selecting sanitation systems in developing countries

Due to the socio-economic inhomogeneity of communities in developing countries, the selection of sanitation systems is a complex task. To assist planners and communities in assessing the suitability of alternatives, the decision support system SANEX™ was developed. SANEX™ evaluates alternatives in two steps. First, Conjunctive Elimination, based on 20 mainly technical criteria, is used to screen feasible alternatives. Subsequently, a model derived from Multiattribute Utility Technique (MAUT) uses technical, socio-cultural and institutional criteria to compare the remaining alternatives with regard to their implementability and sustainability. This paper presents the SANEX™ algorithm, examples of its application in practice, and results obtained from field testing.

Application of Fuzzy Variables for Systems of Management Decision Support

The following article describes an approach covering the variety of opinions and uncertainties of estimates within the chosen technique of decision support. Mathematical operations used for assessment of options are traced to operations of working with functions that are used for assessment of possible options of decision-making. Approach proposed could be used within any technique of decision support based on elementary mathematical operations. In this article the above-mentioned approach is described under analytical hierarchy process.

Evaluation of the multicriteria approval method for timber-harvesting group decision support

Abstract

Decision making and decision support in service systems

This thesis is a literature study that develops a conceptual model of decision making and decision support in service systems. The study is related to the Ä-Logi, Intelligent Service Logic for Welfare Sector Services research project, and the objective of the study is to develop the necessary theoretical framework to enable further research based on the research project results and material. The study first examines the concepts of service and service systems, focusing on understanding the characteristics of service systems and their implications for decision making and decision support to provide the basis for the development of the conceptual model. Based on the identified service system characteristics, an integrated model of service systems is proposed that views service systems through a number of interrelated perspectives that each offer different, but complementary, implications on the nature of decision making and the requirements for decision support in service systems. Based on the model, it is proposed that different types of decision making contexts can be identified in service systems that may be dominated by different types of decision making processes and where different types of decision support may be required, depending on the characteristics of the decision making context and its decision making processes. The proposed conceptual model of decision making and decision support in service systems examines the characteristics of decision making contexts and processes in service systems, and their typical requirements for decision support. First, a characterization of different types of decision making contexts in service systems is proposed based on the Cynefin framework and the identified service system characteristics. Second, the nature of decision making processes in service systems is proposed to be dual, with both rational and naturalistic decision making processes existing in service systems, and having an important and complementary role in decision making in service systems. Finally, a characterization of typical requirements for decision support in service systems is proposed that examines the decision support requirements associated with different types of decision making processes in characteristically different types of decision making contexts. It is proposed that decision support for the decision making processes that are based on rational decision making can be based on organizational decision support models, while decision support for the decision making processes that are based on naturalistic decision making should be based on supporting the decision makers’ situation awareness and facilitating the development of their tacit knowledge of the system and its tasks. Based on the proposed conceptual model a further research process is proposed. The study additionally provides a number of new perspectives on the characteristics of service systems, and the nature of decision making and requirements for decision support in service systems that can potentially provide a basis for further discussion and research, and support the practice alike.

Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

Case-based reasoning to support decision making for managing drinking water quality events in distribution systems

Peer reviewed

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Decision support system with incomplete and domain incoherent information management

Many of the most common human functions such as temporal and non-monotonic reasoning have not yet been fully mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This is mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, recognition of chronicles, fuzzy expert systems, etc. SPARSE (from the Portuguese acronym, which means expert system for incident analysis and restoration support) was one of the developed systems and, in the sequence of its development, came the need to cope with incomplete and/or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA (supervisory control and data acquisition) information retrieval, namely real-time operation, huge amounts of information, etc. This paper presents an architecture for a decision support system, which can solve the presented problems, using a symbiosis of the event calculus and the default reasoning rule based system paradigms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability. A prototype implementation of this system is already at work in the control centre of the Portuguese Transmission Network.

Group decision making and Quality-of-information in e-Health systems

Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One’s knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSSs), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision-making process. In this way, it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services.