Satisficing Optimization

Satisficing Optimization is an optimization framework that, unlike classical optimization which seeks globally optimal solutions, is distinguished by its pursuit of solutions that meet acceptable performance thresholds sufficient to satisfy decision criteria, thereby balancing computational efficiency and decision quality under bounded rationality.

62

Publications

2.2K

Citations

132

Authors

86

Institutions

Publications per year

2017–2026

10

Authors

132

  1. MS
    Masatoshi Sakawa

    Hiroshima University, Japan

    7 publications · 171 citations

    7

    h-index

  2. MI
    Masahiro Inuiguchi

    The University of Osaka, Japan

    5 publications · 92 citations

    5

    h-index

  3. KK
    Kosuke Kato

    Hiroshima University, Japan

    3 publications · 38 citations

    3

    h-index

  4. TI
    Tomohiro Ikeda

    Hiroshima University, Japan

    2 publications · 28 citations

    2

    h-index

  5. DZ
    Daniel Zhuoyu Long

    Chinese University of Hong Kong, Hong Kong SAR China

    2 publications · 125 citations

    2

    h-index

1–5 of 132

Venues

  1. Fuzzy Sets and Systems

    4 publications · 74 citations

    4

    h-index

  2. Operations Research

    2 publications · 134 citations

    2

    h-index

  3. 2

    h-index

  4. European Journal of Operational Research

    2 publications · 54 citations

    2

    h-index

  5. IIE Transactions

    2 publications · 118 citations

    2

    h-index

1–5