Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Simulazione a Eventi Discreti Multi-Obiettivo× | Simulazione Monte Carlo× | |
|---|---|---|
| Campo≠ | Simulazione | Processo decisionale |
| Famiglia≠ | Process / pipeline | MCDM |
| Anno di origine≠ | 1990s–2000s | 1949 |
| Ideatore≠ | Various (DES: Tocher 1963; multi-objective integration: 1990s–2000s OR literature) | Metropolis, N., Ulam, S. |
| Tipo≠ | Simulation-optimization hybrid | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fonte seminale≠ | Kleijnen, J. P. C., & Gaury, E. (2003). Short-term robustness of production management systems: A case study. European Journal of Operational Research, 148(2), 452–465. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alias≠ | MO-DES, Multi-objective DES, Pareto-based discrete-event simulation, DES with multi-objective optimization | — |
| Correlati≠ | 5 | 0 |
| Sintesi≠ | Multi-Objective Discrete-Event Simulation (MO-DES) couples a discrete-event simulation engine with multi-objective optimization to explore trade-offs among two or more conflicting performance measures — such as throughput, cost, and waiting time — across stochastic, time-ordered process models. It is widely applied in manufacturing, logistics, healthcare, and service system design. | 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. |
| ScholarGateInsieme di dati ↗ |
|
|