| dc.description.abstract | Decision support systems are software which are used to develop insight into system behavior and help managers to make effective plans and decisions. Simulation and modeling are the basic weapons which are used to simplify the problem, abstract system behavior, state and explore the relationship among the components of the system, understand system essence and behavior, predict the results and utilize knowledge to help decision maker to make high quality decisions. One type of decision support system addresses the problem to select a choice from many alternatives [George, 1996]. In other words, the problem is to evaluate and rank a finite number of alternatives with respect to a finite number of criteria. Rank computation depends on the values of the criteria variables and their weight values which directly determine the influence of the variables. How to weight each criteria and how the weights influence the preference of the alternatives is a very important part in decision research. Much research has been done in this area, but most of it is subjective. The best weight value should depict the information of the data set and system behavior. Principal component analysis (PCA) can reduce the dimensionality of the data set and simplify the interrelated variables while retaining most of the information presented in the data set. Much research has indicated principal component analysis has an intuitively satisfying interpretation and illustrated its application in areas where judgments are not easy to come by [Ahamad, 1967; Bailey, 1956; Cahalan, 1983; Chang, 1988; Cochran and Home, 1977; Dawkins, 1989; Jolicoeur, 1959; Jolicoeur and Mosimann, 1960; Kloek and Mennes, 1960; Lee and Chang, 1976; Rao, 1964; Sloan, 1983; Wold, 1976]. Dawkins [Dawkins, 1989], using the first principal component of the national track records from principal component analysis. ranked the world track perfonnance. But principal components are influenced by roundoff error, sample data variation and sampling error. How the rank value changes when the weight is changed and what are the intervals of the weights with the restriction that the final ranking of the alternatives does not change? The objective of this research is to explore the application of PCA in decision support systems and investigate the model behavior under small changes in its assumption and its parameters, understand the key variables and their relationships which can most affect the model solutions and corresponding decisions, validate the model and find better and robust solutions for some particular problems. A decision support system is implemented as part of this research. It is implemented using the MS Visual C++ programming language under the MS Windows 95 environment. The system provides a graphical user interface (GUI) to view results. The remainder of this thesis is organized as folJows. Computation of PCA, application of the PCA, sensitivity analysis of the decision systems are studied in Chapter 2. Design and implementation of the system are explained in Chapter 3, also the process, class, architecture and key algorithms were briefly explained in this Chapter. The result and the interface were shown in Chapter 4. Chapter 5 gives conclusion and some directions for future work. | |
