Interval data minmax regret network optimization problems
Discrete Applied Mathematics
An exact algorithm for the robust shortest path problem with interval data
Computers and Operations Research
A constraint satisfaction approach to the robust spanning tree problem with interval data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
On the complexity of the robust spanning tree problem with interval data
Operations Research Letters
The robust spanning tree problem with interval data
Operations Research Letters
A branch and bound algorithm for the robust shortest path problem with interval data
Operations Research Letters
The minimum risk spanning tree problem
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
Some tractable instances of interval data minmax regret problems: bounded distance from triviality
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
Heuristics for the central tree problem
Journal of Heuristics
On robust online scheduling algorithms
Journal of Scheduling
Some tractable instances of interval data minmax regret problems
Operations Research Letters
Operations Research Letters
A 2-approximation for minmax regret problems via a mid-point scenario optimal solution
Operations Research Letters
Minmax regret bottleneck problems with solution-induced interval uncertainty structure
Discrete Optimization
Complexity of the min-max (regret) versions of min cut problems
Discrete Optimization
On the robustness of graham's algorithm for online scheduling
WADS'07 Proceedings of the 10th international conference on Algorithms and Data Structures
On exact solutions for the Minmax Regret Spanning Tree problem
Computers and Operations Research
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The general problem of minimizing the maximal regret in combinatorial optimization problems with interval data is considered. In many cases, the minmax regret versions of the classical, polynomially solvable, combinatorial optimization problems become NP-hard and no approximation algorithms for them have been known. Our main result is a polynomial time approximation algorithm with a performance ratio of 2 for this class of problems.