Stochastic Makespan Minimization

Stochastic Makespan Minimization is a stochastic scheduling problem that seeks to minimize the expected completion time of the last-executing job in a set of jobs with random processing times, thereby distinguishing it from deterministic makespan minimization by explicitly accounting for uncertainty in job durations.

68

Publications

3.1K

Citations

142

Authors

92

Institutions

Publications per year

2017–2026

12

Authors

142

  1. MP
    Michael Pinedo

    New York University, United States

    8 publications · 274 citations

    8

    h-index

  2. MU
    Marc Uetz

    University of Twente, Netherlands

    2 publications · 58 citations

    2

    h-index

  3. JY
    Jinjiang Yuan

    Zhengzhou University, China

    2 publications · 27 citations

    2

    h-index

  4. MJ
    Mengchen Ji

    Chinese Academy of Sciences, China

    2 publications · 34 citations

    2

    h-index

  5. DL
    Deming Lei

    Wuhan University of Technology, China

    2 publications · 62 citations

    2

    h-index

1–5 of 142

Venues

  1. Operations Research

    6 publications · 724 citations

    6

    h-index

  2. Computers & Operations Research

    6 publications · 174 citations

    6

    h-index

  3. European Journal of Operational Research

    5 publications · 337 citations

    5

    h-index

  4. Journal of the Operational Research Society

    OR · 3 publications · 131 citations

    3

    h-index

  5. Journal of Scheduling

    JOS · 2 publications · 119 citations

    2

    h-index

1–5