Fuzzy multi objective optimization: With reference to multi objective transportation problem

In this paper we present a review of the connection between modern era techniques & fuzzy multi objective optimization (FMOO) to deal with its shortcoming and FMOO used in transportation problem. Multi objective optimization represents an interest area of research since most real life problem have a set of conflict objectives. MOO has its root in late nineteenth century welfare economics, in the works of Edge worth & Pareto. But due to some shortcoming faces, researchers attract to FMOO and they use modern era technique like artificial intelligence. Finally we develop a fuzzy linear programming method for solving the transportation problem with fuzzy goals, available supply & forecast demand and showing a frame for fuzzy multi objective transportation problem (FMOTP) solution.           &nbsp

Modelling and optimizing multiple attribute decisions by using fuzzy sets

The purpose of this paper is to present a coherent perspective of modeling and optimizing multiple attribute decisions by using fuzzy sets. In management practice we face most of the time the situation in which a problem have several possible solutions and each solution can be analyzed using multiple criteria models. In the same time, in real life decision making process there is a given level of uncertainty which makes difficult a clear cut analytical analysis. The object of this article is to build a model approach for making multiple criteria decision using fuzzy sets of objects. Elaborating multiple attribute decisions involves performing an assessment and selecting from a given and finite set of possible alternative courses of action in the presence of a given and finite, and usually conflicting set of attributes and criteria.decision making, fuzzy sets, modeling, multiple criteria optimization.

Ranking of fuzzy sets based on the concept of existence

AbstractVarious approaches have been proposed for the comparison or ranking of fuzzy sets. However, due to the complexity of the problem, a general method which can be used for any situation still does not exist. This paper formalizes the concept of existence for the ranking of fuzzy sets. Many of the existing fuzzy ranking methods are shown to be some application of this concept. An improved fuzzy ranking method is then introduced, based on this concept. This newly introduced method is expanded for treating both normal and nonnormal, convex and nonconvex fuzzy sets. Emphasis is placed on the use of the subjectivity of the decision maker, such as the optimistic or the pessimistic view points. An improved procedure for obtaining linguistic conclusions is also developed. Finally, some numerical examples are given to illustrate the approach