Min max min robust (relative) regret combinatorial optimization

Alejandro Crema ()
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Alejandro Crema: Universidad Central de Venezuela

Mathematical Methods of Operations Research, 2020, vol. 92, issue 2, No 2, 249-283

Abstract: Abstract We consider combinatorial optimization problems with uncertainty in the cost vector. Recently, a novel approach was developed to deal with such uncertainties: instead of a single one robust solution, obtained by solving a min max problem, the authors consider a set of solutions obtained by solving a min max min problem. In this new approach, the set of solutions is computed once and we can choose the best one in real time each time a cost vector occurs yielding better solutions compared to the min max approach. In this paper, we apply the new approach to the absolute and relative regret cases. Algorithms to solve the min max min robust (relative) regret problems are presented with computational experiments.

Keywords: Minmax regret; Robust programming; Greedy algorithms; Multiparametric programming (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00186-020-00712-y

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