Simulated annealing: theory and applications
Simulated annealing: theory and applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
An approximation algorithm for interval data minmax regret combinatorial optimization problems
Information Processing Letters
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
Robust solutions of uncertain linear programs
Operations Research Letters
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
Heuristics for the central tree problem
Journal of Heuristics
On exact solutions for the Minmax Regret Spanning Tree problem
Computers and Operations Research
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This paper addresses the robust spanning tree problem with interval data, i.e. the case of classical minimum spanning tree problem when edge weights are not fixed but take their values from some intervals associated with edges. The problem consists of finding a spanning tree that minimizes so-called robust deviation, i.e. deviation from an optimal solution under the worst case realization of interval weights. As it was proven in Kouvelis and Yu (Robust Discrete Optimization and Its Applications, Kluwer Academic, Norwell, 1997), the problem is NP-hard, therefore it is of great interest to tackle it with some metaheuristic approach, namely simulated annealing, in order to calculate an approximate solution for large scale instances efficiently. We describe theoretical aspects and present the results of computational experiments. To the best of our knowledge, this is the first attempt to develop a metaheuristic approach for solving the robust spanning tree problem.