Algorithms for two bottleneck optimization problems
Journal of Algorithms
When upper probabilities are possibility measures
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Parametric Combinatorial Computing and a Problem of Program Module Distribution
Journal of the ACM (JACM)
Using sparsification for parametric minimum spanning tree problems
Nordic Journal of Computing
Interval data minmax regret network optimization problems
Discrete Applied Mathematics
Finding the shortest bottleneck edge in a parametric minimum spanning tree
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Stochastic shortest paths via Quasi-convex maximization
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
On Possibilistic/Fuzzy Optimization
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Minmax regret solutions for minimax optimization problems with uncertainty
Operations Research Letters
Complexity of the min-max and min-max regret assignment problems
Operations Research Letters
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In this paper a general bottleneck combinatorial optimization problem with uncertain element weights modeled by fuzzy intervals is considered. A possibilistic formalization of the problem and solution concepts in this setting, which lead to compute robust solutions under fuzzy weights, are given. Some algorithms for finding a solution according to the introduced concepts and evaluating optimality of solutions and elements are provided. These algorithms are polynomial for bottleneck combinatorial optimization problems with uncertain element weights, if their deterministic counterparts are polynomially solvable.