Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Cross-impact analysis using group decision support systems: an application to the future of Hong Kong

When several interdependent events affect the future of an organization, an industry, or a society, it is often useful to know how these events may affect each other. Determining the impact of external events on other such events, called a cross-impact analysis, is usually accomplished by asking knowledgeable people to (1) discuss any relationships among the events and (2) provide subjective estimates of conditional probabilities relating the events. However, there are two possible problems. First, in some political environments people may be reluctant to discuss the events openly. Second, the subjective probability estimates may violate the laws of probability theory, such as Bayes' theorem. We present a simple method, using group decision support systems (GDSS), for eliciting anonymous comments and preparing consistent probability estimates concerning interdependent events. We then illustrate our method by using it to perform a cross-impact analysis concerning the future of Hong Kong. (C) 1999 Elsevier Science Ltd. All rights reserved

Building scenarios for Hong Kong using EMS

Managers facing substantial uncertainties find it helpful to construct scenarios describing alternative futures. The scenarios reveal a variety of possible futures that managers might face, and the scenario construction process can offer insights into the source of future uncertainties. As part of the scenario-building process it is useful to talk with people who are knowledgeable about the events depicted in the scenarios. But in some political environments, these people may be reluctant to talk openly about their concerns. This article describes an approach based on electronic meeting systems in which participants can discuss sensitive events anonymously through a network of personal computers. The article presents scenarios for the business future of Hong Kong following its reunification with the People's Republic of China. (C) 1998 Elsevier Science Ltd. All rights reserved

An empirical measure of element contribution in neural networks

A frequent complaint about neural net models is that they fail to explain their results in any useful way, The problem is not a lack of information, but an abundance of information that is difficult to interpret. When trained, neural nets will provide a predicted output for a posited input, and they can provide additional information in the form of interelement connection strengths. But this latter information is of little use to analysts and managers who wish to interpret the results they have been given. In this paper, we develop a measure of the relative importance of the various input elements and hidden layer elements, and we use this to interpret the contribution of these components to the outputs of the neural net

Anatomy of Electronic Commerce Infrastructure in Hong Kong and Singapore Using a Generic Framework Periscope

Department of Management and Marketin

Solution of large-scale multi-objective optimization models for saltwater intrusion control in coastal aquifers utilizing ANFIS based linked meta-models for computational feasibility and efficiency

Saltwater intrusion in coastal aquifers poses significant challenges in the management of vulnerable coastal groundwater resources around the world. To develop a strategy for regional scale sustainable management of coastal aquifers, solution of large-scale multi-objective decision models is essential. The flow and solute transport equations are also density dependent, where the flow parameters are dependent on salt concentration; hence, the flow and solute transport equations need to be solved as coupled equations. In a linked optimization simulation model, the numerical simulation model as a predictor of the physical processes need to be solved enormous number of times to be able to identify an optimum solution as per the specified objectives and constraints. This problem becomes even more complicated when multiple objectives are included and the Pareto optimal solutions need to be determined. Therefore, to ensure the computational efficiency and feasibility of determining a regional scale strategy for control and sustainable use of a coastal aquifer, meta-models that are trained, tested and validated using randomized solutions of the numerical simulation models can be utilized. These meta-models once trained and tested serves the purpose of an approximate emulator of the complex numerical models rendering the solution of a complex and large scale linked optimization model computationally efficient and feasible. The optimal groundwater extraction patterns can be obtained through linked simulation-optimization (S/O) technique in which the simulation part is usually replaced by computationally efficient meta-models. This study proposes a computationally efficient meta-model to emulate density reliant integrated flow and solute transport scenarios of coastal aquifers. A meta-model, Adaptive Neuro Fuzzy Inference System (ANFIS) is trained and developed for an illustrative coastal aquifer study area. Prediction accuracy of the developed ANFIS based meta-model is evaluated for suitability. The meta-model is then integrated with a multiple objective coastal aquifer management model to demonstrate the potential application of this methodology. The optimization algorithm utilized for solution is the Controlled Elitist Multi-objective Genetic Algorithm. Performance evaluation results show acceptable accuracy in the obtained optimized management strategies. Therefore, use of trained and tested meta-models linked to an optimization model results in significant computational efficacy. It also ensures computational practicability of solving such large-scale integrated S/O approach for regional scale coastal groundwater management