Σύγκριση μεθόδων

	Διακριτή Προσομοίωση Γεγονότων (DES)	Στοχαστική Βελτιστοποίηση
Πεδίο	Προσομοίωση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1960s (formalized); modern computational form from 1970s onward	1951 (SGD); 2014 (Adam)
Δημιουργός	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)	—
Τύπος	Stochastic process simulation	Gradient-based iterative optimization
Θεμελιώδης πηγή	Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
Εναλλακτικές ονομασίες	DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Συναφείς	4	3
Σύνοψη	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.	Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.