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The results obtained in this chapter extend the possibility of applications of the (⊕, ⊗)-linear systems and optimization problems to cases in which the data of the problems are inexact and the inexactness is expressed by replacing the exact coefficients of the (⊕, ⊗)-linear functions involved with interval coefficients. Such problems occur, e.g., in scheduling problems with inexact processing times, reliability problems with inexact failure probabilities and others. As we have already mentioned, only problems with variables on one side of the constraints were considered here. Problems with variables on both sides of (⊕, ⊗)-linear constraints may become the subject of further research. [ABSTRACT FROM AUTHOR]

In this chapter we have proposed a new general approach to fuzzy single and multicriteria linear programming problems with fuzzy coefficients. A unifying concept of this approach is the concept of a fuzzy relation, particularly fuzzy extension of the inequality or equality relation and the concept of an aggregation operator. We have formulated the FLP problem, defined a feasible solution of the FLP problem and dealt with the problem of "optimal solution" of FLP problems. Two approaches have been introduced: the satisficing solution based on externalgoals modeled by fuzzy quantities, and the α-efficient (nondominated) solution. Then our interest has been focused on the problem of duality in FLP. Finally, we have also dealt with the multicriteria case. We have formulated a fuzzy multicriteria linear programming problem, defined a compromise solution and derived basic results. The chapter has been closed with a numerical example. [ABSTRACT FROM AUTHOR]