Fuzzy logic was originally introduced by Zadeh (1965) as a mathematical way to represent vagueness in everyday life. Fuzzy logic is the logic of approximate rather than exact reasoning. The importance of fuzzy logic derives from the fact that most modes of human reasoning, especially common-sense reasoning, are approximate in nature. Despite its pervasiveness, approximate reasoning falls outside the purview of classical logic mainly because it is a deeply entrenched tradition in logic to be concerned with those and only those modes of reasoning that lend themselves to precise formulation and analysis (Zadeh, 1992). In our everyday life, we assimilate and use (act on) fuzzy data, vague rules, and imprecise information, just as we are able to make decisions about situations that seem to be governed by an element of chance. Accordingly, computational models of real systems should also be able to recognize, represent, manipulate, interpret, and use (act on) both fuzzy and statistical uncertainties. Fuzzy interpretations of data structures are a very natural and plausible way to formulate and solve various problems. Conventional (crisp) sets contain objects that satisfy precise properties required for membership. Crisp sets correspond to two-valued logic: is or not, on or off, black or white, 0 or 1 (Bezdek, 1994). Although fuzzy logic has been known for nearly 30 years, it has only recently appeared in the popular and technical press. Interest in fuzzy models was not very widespread until their utility in technological applications became apparent. Nearly all the attention in fuzzy logic has been focused on the realms of process and control engineering, brought on by the bloom of fuzzy logic products developed in Japan (Cox, 1994). Fuzzy models have supplanted more conventional technologies in many scientific applications over the last 10 years, especially in the realms of control engineering and system analysis. Examples of these applications include air conditioners, TV camcorders, palm-top computers, vacuum cleaners, ship navigators, automobile transmissions, steam turbine controller, and subway controllers (Bezdek, 1994). Several attempts have been made to include fuzzy logic cases in business and management disciplines. Knowledgebased fuzzy models have been applied to site-selection (Narasimhan,<br />1979), determination of production blending composition (Zimmermann, 1986), scheduling TV advertising (Talbot, 1988), designing a logistic system for scheduling the inventory, movement, and availability of containers on containerships (Zimmermann, 1992), analyzing behaviors of the individual members of a group guided by a leader (Kurosu et al., 1994), modeling human judgment capacity in organizational routine<br /><br />م.م. مصطفى حميد