Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Solving fuzzy relation equations with a linear objective function
Fuzzy Sets and Systems
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
A Note on Fuzzy Relation Programming Problems with Max-Strict-t-Norm Composition
Fuzzy Optimization and Decision Making
Fuzzy relation equations for coding/decoding processes of images and videos
Information Sciences—Informatics and Computer Science: An International Journal
Optimization of fuzzy relational equations with max-av composition
Information Sciences: an International Journal
An algorithm for solving fuzzy relation equations with max-T composition operator
Information Sciences: an International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Deriving minimal solutions for fuzzy relation equations with max-product composition
Information Sciences: an International Journal
Development of fuzzy process accuracy index for decision making problems
Information Sciences: an International Journal
On the relation between equations with max-product composition and the covering problem
Fuzzy Sets and Systems
An efficient solution procedure for fuzzy relation equations with max-product composition
IEEE Transactions on Fuzzy Systems
An accelerated approach for solving fuzzy relation equations with a linear objective function
IEEE Transactions on Fuzzy Systems
Solution to the covering problem
Information Sciences: an International Journal
Finitary solvability conditions for systems of fuzzy relation equations
Information Sciences: an International Journal
Linear optimization problem constrained by fuzzy max-min relation equations
Information Sciences: an International Journal
Resolution of a system of the max-product fuzzy relation equations using LºU-factorization
Information Sciences: an International Journal
Some properties of infinite fuzzy relational equations with sup-inf composition
Information Sciences: an International Journal
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The work examines the feasibility of minimizing a linear objective function subject to a max-t fuzzy relation equation constraint, where t is a continuous/Archimedean t-norm. Conventional methods for solving this problem are significantly improved by, first separating the problem into two sub-problems according to the availability of positive coefficients. This decomposition is thus more easily handled than in previous literature. Next, based on use of the maximum solution of the constraint equation, the sub-problem with non-positive coefficients is solved and the size of the sub-problem with positive coefficients reduced as well. This step is unique among conventional methods, owing to its ability to determine as many optimal variables as possible. Additionally, several rules are developed for simplifying the remaining problem. Finally, those undecided optimal variables are obtained using the covering problem rather than the branch-and-bound methods. Three illustrative examples demonstrate that the proposed approach outperforms conventional schemes. Its potential applications are discussed as well.