
Combinatorial twostage minmax regret problems under interval uncertainty
In this paper a class of combinatorial optimization problems is discusse...
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On exact solutions for the minmax regret spanning tree problem
The Minmax Regret Spanning Tree problem is studied in this paper. This i...
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A Heuristic Based on Randomized Greedy Algorithms for the Clustered ShortestPath Tree Problem
Randomized Greedy Algorithms (RGAs) are interesting approaches to solve ...
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An SDP dual relaxation for the Robust Shortest Path Problem with ellipsoidal uncertainty: Pierra's decomposition method and a new primal FrankWolfetype heuristics for duality
This work addresses the Robust counterpart of the Shortest Path Problem ...
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Mixed Uncertainty Sets for Robust Combinatorial Optimization
In robust optimization, the uncertainty set is used to model all possibl...
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Minimum 0Extension Problems on Directed Metrics
For a metric μ on a finite set T, the minimum 0extension problem 0Ext[...
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WiSM: Windowing Surrogate Model for Evaluation of CurvatureConstrained Tours with Dubins vehicle
Dubins tours represent a solution of the Dubins Traveling Salesman Probl...
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Algorithms for the Minmax Regret Path Problem with Interval Data
The Shortest Path in networks is an important problem in Combinatorial Optimization and has many applications in areas like Telecommunications and Transportation. It is known that this problem is easy to solve in its classic deterministic version, but it is also known that it is an NPHard problem for several generalizations. The Shortest Path Problem consists in finding a simple path connecting a source node and a terminal node in an arcweighted directed network. In some realworld situations the weights are not completely known and then this problem is transformed into an optimization one under uncertainty. It is assumed that an interval estimate is given for each arc length and no further information about the statistical distribution of the weights is known. Uncertainty has been modeled in different ways in Optimization. Our aim in this paper is to study the Minmax Regret Path with Interval Data problem by presenting a new exact branch and cut algorithm and, additionally, new heuristics. A set of difficult and large size instances are defined and computational experiments are conducted for the analysis of the different approaches designed to solve the problem. The main contribution of our paper is to provide an assessment of the performance of the proposed algorithms and an empirical evidence of the superiority of a simulated annealing approach based on a new neighborhood over the other heuristics proposed.
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