방법 비교

	이산 사건 시뮬레이션 (DES)	수학적 휴리스틱: 수학적 계획법과 메타휴리스틱의 결합	확률적 최적화
분야	시뮬레이션	최적화	최적화
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1960s (formalized); modern computational form from 1970s onward	2009	1951 (SGD); 2014 (Adam)
창시자	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)	Maniezzo, Stützle & Voß	—
유형	Stochastic process simulation	Hybrid optimization framework	Gradient-based iterative optimization
원전	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	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
별칭	DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)	Hybrid Metaheuristics, MIP-based Heuristics, Math-Programming Hybrids, Matematiksel Sezgisel Yöntemler	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
관련	4	3	3
요약	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.	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.