چکیده:
In this article, first, attention is paid to the type of human attitude and 'cognition' of phenomena in general, and the inadequacy of Aristotelian two-valued logic for recognizing vague phenomena 1 in particular. Then, how fuzzy logic 2 deals with ambiguity and types of uncertainty 3 is examined; to respond to some management challenges arising from uncertainty and ambiguity, the extensive application capabilities of fuzzy logic in management systems are considered. To this end, numerous management systems, including planning, decision-making, and their modeling with a fuzzy approach, are studied so that a better understanding of organizational issues and topics can be provided. From this perspective, a challenge arises for the fuzzy modeling of policy-making systems. In conclusion, a perspective on intelligent fuzzy-neural models 4 is presented as a solution for developing the modeling of management systems.
خلاصه ماشینی:
To this end, various management systems, including planning, decision-making, and their modeling with a fuzzy approach, are studied so that a better understanding of organizational issues and topics can be provided.
With extraordinary flexibility, fuzzy logic is designed for analyzing the meanings of natural language, and it is capable of modeling and analyzing ambiguities arising from the human mind and the environment, as well as the degree of imprecision present in human judgment.
5-Fuzzy Modeling of Management Systems Classic management practice methods are all derived from crisp mathematics 28 and two-valued and multi-valued logic, which require quantitative and precise data.
Today, by using fuzzy set theory, it is possible to proceed with the fuzzy modeling of management systems, taking into account the realities existing in organizations to achieve optimal decisions and policies.
Membersship Function By employing fuzzy systems theory, the methodology of classic management science is extended to a fuzzy environment; fuzzy logic is applicable to various management systems, including decision-making, policymaking, planning, and their modeling.
Necessity measure b) Fuzzy multi-objective programming: In the real world, for planning and decision-making in management and production issues, many conflicting and multiple constraints and objectives must be considered.
(31 and 32 and 29) c) Fuzzy production management: Many mathematical models based on classic logic in the field of planning (39).
Fuzzy logic is one of the most efficient approaches for measuring the imprecision of factors in statistical quality control, so that more realistic information can be obtained for management decision-making.
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