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., & 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
A. Ghodousian; Sara 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
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
VANCOUVER
Ghodousian, A., Falahatkar, S. Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator. Journal of Algorithms and Computation, 2020; 52(2): 13-28. doi: 10.22059/jac.2020.79080
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