Negative dependence in matrix arrangement problems

Jakobsons, Edgars; Wang, Ruodu

doi:10.1007/s10479-017-2725-7

Cited by 1 publication

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“…Other examples are given by L-statistics (linear combinations of order statistics) with decreasing coefficients, thus also the maximum and (minus) the minimum are Schurconvex. 5 We refer to Marshall et al (2011), Rüschendorf (2013), Boland and Proschan (1988) and Jakobsons and Wang (2016) for further details on the compatibility of rearrangement methods with Schur-convex objective functions.…”

Section: Assumption 1 the Functionmentioning

confidence: 99%

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

2017

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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.

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Section: Assumption 1 the Functionmentioning

confidence: 99%