Showing papers by "Dong-Ling Xu published in 2008"

Clinical Decision Support Systems: A Review on Knowledge Representation and Inference Under Uncertainties

Abstract: This paper provides a literature review in clinical decision support systems (CDSSs) with a focus on the way knowledge bases are constructed, and how inference mechanisms and group decision making methods are used in CDSSs. Particular attention is paid to the uncertainty handling capability of the commonly used knowledge representation and inference schemes. The definition of what constitute good CDSSs and how they can be evaluated and validated are also considered. Some future research directions for handling uncertainties in CDSSs are proposed.

Group-based ER-AHP system for product project screening

Abstract: New product development (NPD) is highly risky because of fierce competition, as well as rapid technological and market changes, which results in high rates of NPD project failure. A lot of research has found that the high mortality rate is, to some extent, accountable to the selection of wrong NPD projects. NPD project screening is thus a critical activity adopted in early product development stages to enhance the success rate of NPD projects. The manufacturing companies thus demand more intelligent and advanced tools that can advance their NPD screening decisions. During the NPD process, companies need to develop their new products with better and safer performance, higher quality and reliability, better environmental-friendliness and in shorter time. Such multiple criteria have to be considered and assessed at early product project screening stages, which involves a group of cross-functional experts. Due to a large number of quantitative and qualitative criteria and lack of sufficient and concrete data, it is often the case that group members have to make decisions in uncertain situations. It is therefore a big challenge for product managers and experts to move from experience-based decision making to scientific NPD project screening decision making. This paper proposes a novel methodology by integrating the evidential reasoning (ER) approach and analytic hierarchy process (AHP), which can help manufacturers in handling uncertainties and group-based decisions in the early NPD project screening stage. An ER-AHP based decision support system is then developed. A case study with an electronic consumer product manufacturer is conducted to demonstrate how the developed ER-AHP methodology and decision support system be used to support NPD project screening decisions.

Ship selection using a multiple-criteria synthesis approach

Abstract: A method is presented for selecting a preferred ship from a group of candidates as a reference ship for a new design. The method is based on a recently developed approach for multiple-criteria decision analysis under uncertainty, the evidential reasoning approach. Using this method, both quantitative and qualitative attributes of a complicated nature can be considered in the selection process. The method consists of three phases: identifying suitable candidate ships, evaluating them in terms of both conventional techno-economical and qualitative attributes, and aggregating all the attributes using the evidential reasoning approach. This three-phase procedure is illustrated by means of an oil tanker selection example. The results of this study show that the evidential reasoning approach can support multiple-criteria ship selection processes when both qualitative and quantitative information with or without uncertainties have to be taken into account. The outcomes generated by the method include the ranking of the candidate ships and indications of their strengths and weaknesses in the format of performance distributions over different assessment grades. Such information is vital in helping decision makers to make an informed selection and be aware of any risk implication associated with the selection.

Application of an intelligent decision system to nuclear waste repository option analysis

Abstract: This paper presents how the evidential reasoning approach for multi-criteria decision analysis, with the support of its software implementation, Intelligent Decision System (IDS), can be used to analyse whether low level radioactive waste should be stored at the surface or buried deep underground in the territory of the community of Mol in Belgium. Following an outline of the problem and the assessment criteria, the process of using IDS for data collection, information aggregation and outcome presentation is described. The outcomes of the analysis are examined using the various sensitivity analysis functions of IDS. The analysis using IDS can generate informative outcomes to better support the decision making process in practice.

Handling Uncertain and Qualitative Information in Impact Assessment – Applications of IDS in Policy Making Support

Abstract: Summary. Impact assessment (IA) in policy making processes has received increasing attention in recent years. One of the major challenges in IA is how to rationally handle and make maximum use of information in uncertain and qualitative data so that the best course of action can be reliably identified. It is discussed in this chapter how the Evidential Reasoning (ER) approach for multiple criteria decision analysis (MCDA) can be used to take the challenge. The ER approach and its software implementation, called the Intelligent Decision System (IDS), are developed with a focus on rationally handling a large amount of information of both a qualitative and quantitative nature and possibly with different degrees of uncertainties in assessment problems. It applies belief decision matrices for problem modelling so that different formats of available data and uncertain knowledge can be incorporated into assessment processes. It uses an evidential reasoning process on the data to generate assessment outcomes that are informative, rational and reliable. Several examples are examined to demonstrate how IDS can be used to support activities in different stages of an IA process, namely (a) problem structuring, (b) assessment model building, including value elicitation, (c) data collection, management, and aggregation, and (d) data presentation and sensitivity analysis. This investigation shows that IDS is not only a versatile assessment supporting tool, but also a knowledge management tool which helps to organise assessment knowledge and data systematically for better traceability, consistency and efficiency in assessment.