Stochastic Replacement Models

Stochastic Replacement Models is a mathematical framework within replacement decision theory that incorporates random variables and probabilistic outcomes to model the optimal timing and conditions for replacing systems or components under uncertainty.

252

Publications

12.6K

Citations

316

Authors

211

Institutions

Publications per year

2017–2026

32

Authors

316

  1. TN
    Toshio Nakagawa

    Aichi Institute of Technology, Japan

    21 publications · 1K citations

    18

    h-index

  2. SS
    Shey‐Huei Sheu

    National Taiwan University of Science and Technology, Taiwan

    21 publications · 669 citations

    17

    h-index

  3. XZ
    Xufeng Zhao

    Nanjing University of Aeronautics and Astronautics, China

    8 publications · 275 citations

    8

    h-index

  4. RM
    Richard M. Feldman

    Texas A&M University, United States

    6 publications · 1K citations

    6

    h-index

  5. ZG
    Zhe George Zhang

    Western Washington University, United States

    6 publications · 119 citations

    6

    h-index

1–5 of 316

Venues

  1. Journal of Applied Probability

    JAP · 26 publications · 942 citations

    20

    h-index

  2. IEEE Transactions on Reliability

    25 publications · 909 citations

    18

    h-index

  3. Naval Research Logistics (NRL)

    14 publications · 1.3K citations

    14

    h-index

  4. European Journal of Operational Research

    14 publications · 723 citations

    14

    h-index

  5. Management Science

    13 publications · 522 citations

    12

    h-index

1–5