Chebyshev Scalarization

Chebyshev Scalarization is a scalarization method in multi-objective Markov decision processes that transforms a vector-valued objective into a single objective by minimizing the weighted Chebyshev distance between the expected reward vector and a reference point, thereby accommodating non-convex preference structures distinct from linear scalarizations.

5

Publications

339

Citations

15

Authors

10

Institutions

Publications per year

2017–2026

3

Authors

15

  1. WC
    Wei Chen

    Capital University of Economics and Business, China

    1 publications · 16 citations

    1

    h-index

  2. JZ
    Jinhuan Zhang

    Clemson University, United States

    1 publications · 16 citations

    1

    h-index

  3. SW
    Susan Wei

    The University of Melbourne, Australia

    1 publications · 17 citations

    1

    h-index

  4. HW
    Hua Wang

    Tongji University, China

    1 publications · 24 citations

    1

    h-index

  5. KV
    Kristof Van Moffaert

    Vrije Universiteit Brussel, Belgium

    1 publications · 251 citations

    1

    h-index

1–5 of 15

Venues

  1. Journal of Multi-Criteria Decision Analysis

    1 publications · 16 citations

    1

    h-index

  2. IEEE Transactions on Antennas and Propagation

    1 publications · 31 citations

    1

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. Computational Optimization and Applications

    1 publications · 17 citations

1–5