The Mothodology Of Mutil-Attribute Decision Making Based On Rough Set Theory

With the advancement progress of society and the fast development of technology, the researches on the theories and approaches of multiple-attribute decision making have receive great achievements in management science. However, as the result of the information system is becoming more complex and constantly changing all the time in nowadays, the approaches of how to adjust the conflict between single target and multiple targets (criteria), static environment and dynamic environment; how to acquire the reasonable and efficiency weight and rule mining mechanism, how to obtain the dynamic decision rules and updating strategies for generating knowledge, have become the new keystone and difficult in decision making problems. By considering of the semantic environment in management decisions, rough set theory is induced into multiple-attribute decision procedure. The problems of how to choose the property rough set models and how to use them in management decision system is clearly discussed in our paper. In addition, the two problems about weight setting and rules acquirement are proposed step by step to illuminate the theories and approaches of the multiple-decision making, respectively.Firstly, observed by the lack the weight setting, rough set theory as well as information entropy are induced into multiple-attribute decision making, an approach for attribute weights acquisition based on rough sets theory and information gain is propose to overcome the subjectivity and redundancy, and our approach is also compare with other methods (i.e. AHP).Secondly, with the insightful gain from the ordinality and inconsistency in real management information system simultaneously, the preference-orders relation is induced into Probabilistic rough sets model (PRS), and a Probabilistic model of strict-dominance-based rough set approaches (P-SDRSA) based on complete information system and incomplete information system are proposed respectively. Due to these approaches consider the flexibility. problem and the "order" character in decision making process, it make the decision result more reasonable and suitable for us to acquire the decision rules in management decision environment.In addition, considering the changes of environment, three different models and strategies are proposed for the knowledge incremental learning in dynamic decision system, including the following three cases:(1) The object set in the information system evolves over time while the attribute set remains constant; (2) The attribute set in the information system evolves over time while the object set remains constant; (3) The attribute value in the information system evolves over time while the object set and attribute set remain constant. These models give a series of new approaches to deal with the dynamic decision problems. Furthermore, the experiments results not only provide the efficiency of our approaches, but also make the decision process simpler and more clearly, which gives us a new viewpoint to solve the dynamic decision puzzles.Overall, rough set theory in mathematic and data mining technologies in computer science are integrated into multiple-attribute decision making in this paper, a new approach based on the view of management information system is set up to solve the multiple-attribute decision problems from simple to complex, from static to dynamic, which remedy the defects of classical multiple-attribute decision methods somehow. In a short, these researches have some theoretical significance and clinical value in multiple-attribute decision making studies.