Nikolaos F. Matsatsinis

MCDA and preference disaggregation in group decision support systems

Abstract Within the frame of decision aid literature, group decision making has drawn the attention of researchers from a wide spectrum of disciplines. Group Decision Support Systems (GDSS) can play a critical role, in decision situations with multiple individuals, each having his/her own private point of view on the handling of the decision problem. In such an environment, the conflict between the members of the group is not a seldom situation. Multiple criteria decision aid (MCDA) methods can be proven as invaluable tools in handling such interpersonal conflicts where the aim is to achieve consensus between the group members or at least reduce the amount of conflict among participating individuals. This paper reviews some of the past approaches in the multiple criteria–multiple decision makers context.

Knowledge acquisition and representation for expert systems in the field of financial analysis

Abstract Knowledge acquisition and representation has been characterised as the major bottleneck in the development of expert systems (Barr & Geigenbaum, 1982), especially in problem domains of high complexity. Financial analysis is one of the most complicated practical problems, where the expert systems technology is highly applicable, mainly because of its symbolic reasoning and its explanation capabilities. The aim of this paper is to present a complete methodology for knowledge acquisition and representation for expert systems development in the field of financial analysis. This methodology has been implemented in the development of the FINEVA multicriteria knowledge-based decision support system for the assessment of corporate performance and viability. The application of this methodology in the development of the FINEVA system is presented.

MARKEX: An intelligent decision support system for product development decisions

A new methodology for the development of new products and an intelligent DSS, named MARKEX, which is an implementation of this methodology, are presented in this paper. The system acts as a consultant for marketers, providing visual support to enhance understanding and to overcome lack of expertise. The databases of the system are the results of consumer surveys, as well as financial information of the enterprises involved in the decision making process. The systems model base encompasses statistical analysis, preference analysis, and consumer choice models. MARKEX incorporates partial knowledge bases to support decision makers in different stages of the product development process.

On the use of knowledge-based decision support systems in financial management: a survey

Abstract This paper presents an extended survey of the application of knowledge-based decision support systems (KBDSSs) in financial management. KBDSSs originated from the combination of decision support systems with expert system (ES) technology. Thus, initially, the implementation of both decision support systems and ESs in several fields of financial management is discussed. The existing problems and limitations of these two approaches are outlined, and the new methodological framework based on the use of KBDSSs and its application in financial management are presented.

Intelligent Decision Support Methods

The effort to create machines with some sort of intelligence began almost 100 years ago with the ideas of Babbage (1884) as shown in Figure 1. In 1950, Alan Turin, the “father of Artificial Intelligence” (Barr and Feigenbaum, 1981), presented the famous Turing test, which gives an answer to the question if a machine is able to think as a human being (Rich, 1983). Turing not only developed a simple, general and non-arithmetic computational model, but he also supported that computational models could possibly behave with a way that could be deemed “intelligent”. In 1950, Shannon supported that someone could play a game of chess with the help of computer and in 1955 he proved his idea by developing a chess program while, later Samuel (1963) developed a checkers program. Wiener (1948), founder of cybernetics, contributed by recognizing the similarities in functions of humans and machines. In overall, the actual goal of this effort was to understand and find a solution on how to embody in a computer the ability of human beings to think and rationalize (Durkin, 1994). The term “Artificial Intelligence” was used for the first time by John McCarthy (1963; 1969; 1977; 1980; 1995) during a conference held in Dartmouth College (1956).

Towards a decision support system for the ready concrete distribution system: A case of a Greek company

Abstract The aim of this paper is to present an approach to the design of a Decision Support System for the dynamic routing of the various types of vehicles that are necessary for the daily distribution of the ready concrete product. This paper consists of two parts: in the first part the problem is analytically presented, thus setting the basics for building the problem’s model. At the same time special focus has been devoted in analyzing the corresponding parameters. In the second part the effort is to determine the information needed and the information available with respect to the techniques and the methods that are used in the optimization process. In addition, the workflow of the routing and scheduling procedures are presented. This is followed by the presentation of a prototype Decision Support System.

A multicriteria DSS for stock evaluation using fundamental analysis

Abstract The paper describes a multicriteria decision support system which aims at presenting an evaluation of the Athens Stock Exchange (ASE) stocks, on the basis of fundamental analysis. The system evaluates the stocks based on the method of fundamental analysis ratios, which is the most appropriate evaluation approach regarding investment decisions within a long term horizon. In addition to quantitative data deriving from fundamental analysis, the system uses qualitative data as well, in order to improve the reliability of the evaluation. The system introduced in this paper, utilises multicriteria analysis methodologies in order to rank the stocks by placing the best stock first and the worst last. Stock evaluation considers the specific characteristics of the potential investor, as well as his attitude towards undertaken risk. The final output of the system is four stock rankings which respond to four different criteria groups, depending on the type of accounting plan each listed company belongs to. The system incorporates a large volume of relevant information and operates in ‘real world conditions’ since its data are constantly updated. Finally, the system is intended for both institutional and private investors.

UTA-Rec: a recommender system based on multiple criteria analysis

UTARec, a Recommender System that incorporates Multiple Criteria Analysis methodologies is presented. The systems performance and capability of addressing certain shortfalls of existing Recommender Systems is demonstrated in the case of movie recommendations. UTARecs accuracy is measured in terms of Kendalls tau and ROC curve analysis and is also compared to a Multiple Rating Collaborative Filtering (MRCF) approach. The results indicate that the proposed Multiple Criteria Analysis methodology can certainly improve the recommendation process by producing highly accurate results, from a user oriented perspective.

A hybrid discrete Artificial Bee Colony - GRASP algorithm for clustering

This paper presents a new hybrid algorithm, which is based on the concepts of the Artificial Bee Colony (ABC) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines an Artificial Bee Colony Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. As the feature selection problem is a discrete problem, a modification of the initially proposed Artificial Bee Colony optimization algorithm, a Discrete Artificial Bee Colony optimization algorithm, is proposed in this study. The performance of the algorithm is compared with other popular metaheuristic methods like classic genetic algorithms, tabu search, GRASP, ant colony optimization, particle swarm optimization and honey bees mating optimization algorithm. In order to assess the efficacy of the proposed algorithm, this methodology is evaluated on datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the corrected clustered samples is very high and is larger than 98%.

Intelligent Support Systems for Marketing Decisions

Preface. Part I: Marketing Decisions. 1. Decision Analysis and Support. 2. The Structure of Marketing Decisions. 3. Strategic Marketing Decisions. Part II: Intelligent Support Systems. 4. Information Systems. 5. Decision Support Systems. 6. Advanced Decision Systems. 7. Intelligent Decision Support Methods. 8. Intelligent Decision Support Systems in Marketing. 9. New Product Development Methodology. 10. Analysis and Design of MARKEX. 11. Applications in Marketing. References. Index.