Benders Decomposition

Benders Decomposition is a decomposition algorithm in mathematical optimization that separates a complex problem into a master problem and one or more subproblems, distinguishing itself by iteratively generating Benders cuts—valid inequalities derived from dual information of the subproblems—to refine the master problem toward global optimality in large-scale, stochastic, or integer programming contexts.

1.1K

Publications

68.2K

Citations

2.3K

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787

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Publications per year

2017–2026

491

Authors

2.3K

  1. MS
    Mohammad Shahidehpour

    Illinois Institute of Technology, United States

    41 publications · 5.1K citations

    33

    h-index

  2. JC
    Jean‐François Cordeau

    HEC Montréal, Canada

    17 publications · 2.1K citations

    16

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  3. ZH
    Zhu Han

    University of Houston, United States

    13 publications · 544 citations

    13

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  4. HÜ
    Halit Üster

    Texas A&M University, United States

    12 publications · 877 citations

    12

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  5. ZL
    Zuyi Li

    Illinois Institute of Technology, United States

    11 publications · 2.5K citations

    11

    h-index

1–5 of 2.3K

Venues

  1. IEEE Transactions on Power Systems

    127 publications · 13.6K citations

    64

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  2. Transportation Science

    51 publications · 4.1K citations

    37

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  3. European Journal of Operational Research

    68 publications · 3.5K citations

    36

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  4. IEEE Transactions on Smart Grid

    28 publications · 2K citations

    24

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  5. Computers & Operations Research

    32 publications · 1.9K citations

    23

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