Aggregate production planning (APP) determines the optimal production plan for the medium term planning horizon. The purpose of the APP is effective utilization of existing capacities through facing the fluctuations in demand. Recently, fuzzy approaches have been applied for APP focusing on vague nature of cost parameters. Considering the importance of coping with customer demand in different p...

This paper the authors propose a new method of intelligent search called vague-search to find the most suitable match for the predicates to answer any imprecise query made by the database users. The method is based on the theory of vague sets introduced by Gau and Buehrer. A corresponding fuzzy method could be generated a special case of our proposed method it is also to be mentioned that the v...

In fuzzy domain, a variable (vague linguistic term) often depends not only on a single variable but on more then one variables. In such a situation multiple regression analysis is more appropriate than simple regression analysis involving one independent variable. This paper introduces fuzzy multiple regression equations of fuzzy sets those are treated as a variable with certain values assigned...

Fuzziness must be considered in systems where available information is uncertain. A model of such a vague phenomenon might be represented as a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM). This integration preserves the benefits of SV...

Six Sigma is a strategy for achieving process improvement and operational excellence within an organization. Decisions on critical parameter selection in analysis phase are always very crucial; it plays a primary role in successful execution of Six Sigma project and for productivity improvement in manufacturing environment and involves the imprecise, vague and uncertain information. Using a cas...

Data mining, also called knowledge discovery in databases, is regarded as a non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable knowledge in large-scale data. This paper briefly reviews some typical applications and highlights potential contributions that fuzzy set theory can make to data mining. In this connection, some advantages of fuzzy methods...

In decision-making problems, decision-makers using fuzzy preference relations express their opinions. The values of fuzzy preference relations have been crisp in recent seminar papers. To allow decision makers to give vague or imprecise responses when they are in the process of comparing two alternatives, this paper proposes a new method using fuzzy linguistic assessment variables instead of cr...

Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to...

During the past decade, knowledge representation research in AI has generated a class of languages called term subsumption languages (TSL), which is a knowledge representation formalism with a well-defined logic-based semanticsDue to its formal semantics, a term subsumption system can automatically infer the subsumption relationships between concepts defined in the system. However, these system...