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

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  • Adam Kasperski
  • Paweł Zieliński

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

Suggested Citation

  • Adam Kasperski & Paweł Zieliński, 2009. "A randomized algorithm for the min-max selecting items problem with uncertain weights," Annals of Operations Research, Springer, vol. 172(1), pages 221-230, November.
    • Handle: RePEc:spr:annopr:v:172:y:2009:i:1:p:221-230:10.1007/s10479-009-0564-x
    DOI: 10.1007/s10479-009-0564-x
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    3. Hamza, Kais, 1995. "The smallest uniform upper bound on the distance between the mean and the median of the binomial and Poisson distributions," Statistics & Probability Letters, Elsevier, vol. 23(1), pages 21-25, April.
    4. Gang Yu, 1996. "On the Max-Min 0-1 Knapsack Problem with Robust Optimization Applications," Operations Research, INFORMS, vol. 44(2), pages 407-415, April.
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    1. repec:wut:journl:v:2:y:2012:id:1022 is not listed on IDEAS
    2. Bogusz Przybysławski & Adam Kasperski, 2012. "A computational study of approximation algorithms for a minmax resource allocation problem," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 22(2), pages 35-43.

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