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Block rearranging elements within matrix columns to minimize the variability of the row sums

Author

Listed:
  • Kris Boudt

    (Vrije Universiteit Brussel (VUB)
    Vrije Universiteit Amsterdam)

  • Edgars Jakobsons

    (ETH Zürich)

  • Steven Vanduffel

    (Vrije Universiteit Brussel (VUB))

Abstract

Several problems in operations research, such as the assembly line crew scheduling problem and the k-partitioning problem can be cast as the problem of finding the intra-column rearrangement (permutation) of a matrix such that the row sums show minimum variability. A necessary condition for optimality of the rearranged matrix is that for every block containing one or more columns it must hold that its row sums are oppositely ordered to the row sums of the remaining columns. We propose the block rearrangement algorithm with variance equalization (BRAVE) as a suitable method to achieve this situation. It uses a carefully motivated heuristic—based on an idea of variance equalization—to find optimal blocks of columns and rearranges them. When applied to the number partitioning problem, we show that BRAVE outperforms the well-known greedy algorithm and the Karmarkar–Karp differencing algorithm.

Suggested Citation

  • Kris Boudt & Edgars Jakobsons & Steven Vanduffel, 2018. "Block rearranging elements within matrix columns to minimize the variability of the row sums," 4OR, Springer, vol. 16(1), pages 31-50, March.
    • Handle: RePEc:spr:aqjoor:v:16:y:2018:i:1:d:10.1007_s10288-017-0344-4
    DOI: 10.1007/s10288-017-0344-4
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    References listed on IDEAS

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    Cited by:

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    4. Jose Blanchet & Henry Lam & Yang Liu & Ruodu Wang, 2020. "Convolution Bounds on Quantile Aggregation," Papers 2007.09320, arXiv.org, revised May 2023.

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