विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| सिमुलेशन-सहायता प्राप्त सांख्यिकीय प्रक्रिया नियंत्रण× | मोंटे कार्लो सिमुलेशन× | |
|---|---|---|
| क्षेत्र≠ | प्रयोगात्मक अभिकल्प | निर्णयन |
| परिवार≠ | Process / pipeline | MCDM |
| उद्भव वर्ष≠ | 1980s–present | 1949 |
| प्रवर्तक≠ | Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward | Metropolis, N., Ulam, S. |
| प्रकार≠ | Hybrid quantitative method | Robustness wrapper — Monte Carlo uncertainty propagation |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC | — |
| संबंधित≠ | 6 | 0 |
| सारांश≠ | 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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