Reverse maximum flow problem under the weighted Chebyshev distance
RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 4-5, pp. 1107-1121

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Given a network G(V, A, u) with two specific nodes, a source node s and a sink node t, the reverse maximum flow problem is to increase the capacity of some arcs (i, j) as little as possible under bound constraints on the modifications so that the maximum flow value from s to t in the modified network is lower bounded by a prescribed value v0. In this paper, we study the reverse maximum flow problem when the capacity modifications are measured by the weighted Chebyshev distance. We present an efficient algorithm to solve the problem in two phases. The first phase applies the binary search technique to find an interval containing the optimal value. The second phase uses the discrete type Newton method to obtain exactly the optimal value. Finally, some computational experiments are conducted to observe the performance of the proposed algorithm.

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DOI : 10.1051/ro/2017088
Classification : 90C35, 90B10, 05C21
Keywords: Maximum flow problem, reverse problem, Chebyshev distance, network design, Newton method, binary search

Tayyebi, Javad 1 ; Mohammadi, Abumoslem 1 ; Kazemi, Seyyed Mohammad Reza 1

1
@article{RO_2018__52_4-5_1107_0,
     author = {Tayyebi, Javad and Mohammadi, Abumoslem and Kazemi, Seyyed Mohammad Reza},
     title = {Reverse maximum flow problem under the weighted {Chebyshev} distance},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
     pages = {1107--1121},
     publisher = {EDP-Sciences},
     volume = {52},
     number = {4-5},
     year = {2018},
     doi = {10.1051/ro/2017088},
     mrnumber = {3878615},
     zbl = {1411.90346},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/ro/2017088/}
}
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Tayyebi, Javad; Mohammadi, Abumoslem; Kazemi, Seyyed Mohammad Reza. Reverse maximum flow problem under the weighted Chebyshev distance. RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 4-5, pp. 1107-1121. doi: 10.1051/ro/2017088

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