A randomized algorithm for the min-max selecting items problem with uncertain weights

Adam Kasperski and Paweł Zieliński ()

Annals of Operations Research, 2009, vol. 172, issue 1, 230 pages

Abstract: This paper deals with the min-max version of the problem of selecting p items of the minimum total weight out of a set of n items, where the item weights are uncertain. The discrete scenario representation of uncertainty is considered. The computational complexity of the problem is explored. A randomized algorithm for the problem is then proposed, which returns an O(ln K)-approximate solution with a high probability, where K is the number of scenarios. This is the first approximation algorithm with better than K worst case ratio for the class of min-max combinatorial optimization problems with unbounded scenario set. Copyright Springer Science+Business Media, LLC 2009

Keywords: Minmax; Selecting items; Randomized algorithm; Robust optimization (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s10479-009-0564-x

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