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| Diszkrét Esemény Szimuláció (DES)× | Matheuristics: Matematikai programozás és metaheurissztikák hibridizálása× | |
|---|---|---|
| Tudományterület≠ | Szimuláció | Optimalizálás |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1960s (formalized); modern computational form from 1970s onward | 2009 |
| Megalkotó≠ | Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s) | Maniezzo, Stützle & Voß |
| Típus≠ | Stochastic process simulation | Hybrid optimization framework |
| Alapmű≠ | Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127 | Maniezzo, V., Stützle, T., & Voß, S. (Eds.). (2009). Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer. ISBN: 978-1-4419-1305-0 |
| Alternatív nevek≠ | DES, event-driven simulation, Ayrık Olay Simülasyonu (DES) | Hybrid Metaheuristics, MIP-based Heuristics, Math-Programming Hybrids, Matematiksel Sezgisel Yöntemler |
| Kapcsolódó≠ | 4 | 3 |
| Összefoglaló≠ | Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time. | Matheuristics is a class of hybrid optimization methods that tightly couple exact mathematical programming components—such as mixed-integer programming (MIP) solvers—with metaheuristic search procedures. Formally introduced and named by Maniezzo, Stützle, and Voß in 2009, the framework leverages the global-search capability of metaheuristics and the structural exploitation of mathematical programming to tackle large-scale combinatorial optimization problems that neither approach can solve effectively alone. |
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