The Robust Set Covering Problem with interval data

Jordi Pereira () and Igor Averbakh ()

Annals of Operations Research, 2013, vol. 207, issue 1, 217-235

Abstract: We study the Set Covering Problem with uncertain costs. For each cost coefficient, only an interval estimate is known, and it is assumed that each coefficient can take on any value from the corresponding uncertainty interval, regardless of the values taken by other coefficients. It is required to find a robust deviation (also called minmax regret) solution. For this strongly NP-hard problem, we present and compare computationally three exact algorithms, where two of them are based on Benders decomposition and one uses Benders cuts in the context of a Branch-and-Cut approach, and several heuristic methods, including a scenario-based heuristic, a Genetic Algorithm, and a Hybrid Algorithm that uses a version of Benders decomposition within a Genetic Algorithm framework. Copyright Springer Science+Business Media, LLC 2013

Keywords: Combinatorial optimization; Heuristics; Genetic algorithms; Minmax regret optimization; Set Covering Problem; Benders decomposition; Branch-and-Cut (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10479-011-0876-5

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