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A Rough Programming Model Based on the Greatest Compatible Classes and Synthesis Effect
Author(s) -
Li Fachao,
Jin Chenxia,
Jing Ying,
WilamowskaKorsak Marzena,
Bi Zhuming
Publication year - 2013
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2175
Subject(s) - randomness , computer science , rough set , operations research , artificial intelligence , management science , industrial engineering , mathematical optimization , mathematics , engineering , statistics
The globalization connects different parts of the world tightly, one region can be closely interacted by another region. The globalized environment can become dynamic and turbulent, thus brings uncertainties into decision making. A critical challenge in system science is to deal with the uncertainties such as fuzziness, randomness and roughness of information. In this paper, a programming model in rough sets is presented. First, the characteristics and limitations of the existing rough programming methods are analysed systematically. Second, the necessity and feasibility of developing a new rough programming model is discussed, and the model is developed on the basis of the greatest compatible classes and synthesis effect. Finally, the effectiveness and characteristics of the newly developed model are validated through a case study. The result illustrates that the new programming model is of significance in practical applications, and it makes it possible to take decision preferences into account of the decision‐making processes effectively. Copyright © 2013 John Wiley & Sons, Ltd.