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A randomized algorithm for the min-max selecting items problem with uncertain weights
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DOI：10.1007/s10479-009-0564-x.png)
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Abstract
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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.
Keywords:
Minmax
Selecting items
Randomized algorithm
Robust optimization
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