Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uigaji wa Matukio Yanayojitokeza kwa Malengo Mengi× | Uigaji wa Matukio Diskriti wa Kistohastiki× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1990s–2000s | 1960s–1970s |
| Mwanzilishi≠ | Various (DES: Tocher 1963; multi-objective integration: 1990s–2000s OR literature) | Banks, Carson, Nelson, Nicol; Law, A. M. |
| Aina≠ | Simulation-optimization hybrid | Stochastic simulation model |
| Chanzo asilia≠ | 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 ↗ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 |
| Majina mbadala | MO-DES, Multi-objective DES, Pareto-based discrete-event simulation, DES with multi-objective optimization | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | 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. | Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals. |
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