Interactive Decision Support Algorithm and Its Application

On the bases of the properties of abstract hierarchical structure model and the concrete structure of the mode1 system. which is convenient to solve practical problems, a visual interactive hierarchical coordination method has been proposed. In this paper, a compensation adjustment sub-mode1 for hydropower stations, an optimal operation sub-model for hydro-thermal power systems, and an aggregation model based on the aspiration level theory are built, and these models can be solved with decision support algorithm. The set of objectives and its structure could be made by the decision-maker in visua1 software, which could be decided by AHP. Finally, the application results show that this methodology is feasible, however, the software (DSS) needs further improvement.

MINIMUM ATTRIBUTE CO-REDUCTION ALGORITHM BASED ON MULTILEVEL EVOLUTIONARY TREE WITH SELF-ADAPTIVE SUBPOPULATIONS

Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient minimum attribute reduction algorithm is proposed based on the multilevel evolutionary tree with self-adaptive subpopulations.A model of multilevel evolutionary tree with self-adaptive subpopulations is constructed,and interacting attribute sets are better decomposed into subsets by the self-adaptive mechanism of elitist populations.Moreover it can self-adapt the subpopulation sizes according to the historical performance record so that interacting attribute decision variables are captured into the same grouped subpopulation,which will be extended to better performance in both quality of solution and competitive computation complexity for minimum attribute reduction.The conducted experiments show the proposed algorithm is better on both efficiency and accuracy of minimum attribute reduction than some representative algorithms.Finally the proposed algorithm is applied to magnetic resonance image(MRI)segmentation,and its stronger applicability is further demonstrated by the effective and robust segmentation results.

A Decision Support Model and Procedure for Selecting Interrelated R&D Projects

A decision support model with stochastic multiobjective functions and constraints and its solution procedure are presented for selecting R&D projects, in which the uncertainty of project evaluation and selection, the interactions of technique, resource and benefit among projects, and the experience, knowledge and preference of R&D managers are considered. The statistical results of stochastic factors representing the benefit contributions and the three kinds of interactions corresponding to each objective among projects are obtained by the Delphi method and QS/NI decision process for developing the model. In the decision analysis solution procedure, experiences and preferences of R&D managers to make decisions are considered by means of aspiration goal levels(AGL), probabilities to reach AGL called satisfied degrees, and probabilities to satisfy the corresponding constraints called feasible degrees, under which a project selection plan can be proposed by the decision analyst or computer. The utility of this model and solution procedure are demonstrated by a real world case example in R&D program of strength and vibration of aircraft turbine engines. which involves 23 project candidates. The evalutions of benefit contributions and interactions of projects are made by 41 experts in the field.