方法对比
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| Simulation-Assisted Statistical Process Control× | 蒙特卡洛模拟× | |
|---|---|---|
| 领域≠ | 实验设计 | 决策 |
| 方法族≠ | Process / pipeline | MCDM |
| 起源年份≠ | 1980s–present | 1949 |
| 提出者≠ | Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward | Metropolis, N., Ulam, S. |
| 类型≠ | Hybrid quantitative method | Robustness wrapper — Monte Carlo uncertainty propagation |
| 开创性文献≠ | Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| 别名≠ | Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC | — |
| 相关≠ | 6 | 0 |
| 摘要≠ | Simulation-assisted statistical process control (SA-SPC) combines computer simulation — typically Monte Carlo or discrete-event simulation — with classical SPC methods to design, test, and calibrate control charts and monitoring schemes before or alongside deployment on a real production process. Rather than relying solely on closed-form analytical assumptions, SA-SPC uses simulated data to evaluate chart performance under realistic, often non-normal process conditions. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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