Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Имитационное моделирование очередей с множеством целей× | Имитационное моделирование дискретных событий (DES)× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s–2000s | 1960s (formalized); modern computational form from 1970s onward |
| Автор метода≠ | Operations research community (Banks, Deb, and related authors) | Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s) |
| Тип≠ | Simulation-based multi-objective optimization | Stochastic process simulation |
| Основополагающий источник≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Pearson Prentice Hall. ISBN: 9780136062127 | Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127 |
| Другие названия≠ | MOQS, Multi-criteria Queueing Simulation, Multi-objective Queue Optimization, Pareto Queueing Simulation | DES, event-driven simulation, Ayrık Olay Simülasyonu (DES) |
| Связанные | 4 | 4 |
| Сводка≠ | Multi-objective queueing simulation combines discrete-event queueing models with multi-objective optimization to simultaneously evaluate and optimize conflicting performance metrics — such as average wait time, server utilization, throughput, and service cost — across a simulated queuing system. It produces a Pareto front of non-dominated solutions rather than a single optimal point, enabling decision-makers to understand trade-offs explicitly. | 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. |
| ScholarGateНабор данных ↗ |
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