In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated whereby the feasible region is formed as the intersection of two inequality fuzzy systems and \textquotedblleft Fuzzy Max-Min\textquotedblright \ averaging operator is considered as fuzzy composition. It is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. An algorithm is presented to solve the problem and an example is described to illustrate the algorithm.
Ghodousian,A and Falahatkar,S . (2020). Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator. Journal of Algorithms and Computation, 52(2), 13-28. doi: 10.22059/jac.2020.79080
MLA
Ghodousian,A , and Falahatkar,S . "Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator", Journal of Algorithms and Computation, 52, 2, 2020, 13-28. doi: 10.22059/jac.2020.79080
HARVARD
Ghodousian A, Falahatkar S. (2020). 'Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator', Journal of Algorithms and Computation, 52(2), pp. 13-28. doi: 10.22059/jac.2020.79080
CHICAGO
A Ghodousian and S Falahatkar, "Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator," Journal of Algorithms and Computation, 52 2 (2020): 13-28, doi: 10.22059/jac.2020.79080
VANCOUVER
Ghodousian A, Falahatkar S. Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator. J. Algo. Comp.. 2020;52(2):13-28. doi: 10.22059/jac.2020.79080
