So sánh phương pháp

	Simheuristics: Kết hợp mô phỏng với siêu heuristic để tối ưu hóa ngẫu nhiên	Mô phỏng sự kiện rời rạc (DES)	Matheuristics: Kết hợp Lập trình Toán học và Siêu nghiệm thức	Tối ưu hóa ngẫu nhiên
Lĩnh vực	Tối ưu hóa	Mô phỏng	Tối ưu hóa	Tối ưu hóa
Họ	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	2015	1960s (formalized); modern computational form from 1970s onward	2009	1951 (SGD); 2014 (Adam)
Người khởi xướng	Juan et al.	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)	Maniezzo, Stützle & Voß	—
Loại	Hybrid simulation-optimization framework	Stochastic process simulation	Hybrid optimization framework	Gradient-based iterative optimization
Công trình gốc	Juan, A. A., et al. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72.	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.
Tên gọi khác	Simulation-based Metaheuristics, Stochastic Metaheuristics with Simulation, Hybrid Simulation-Optimization, Simülistik Sezgiseller	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
Liên quan	3	4	3	3
Tóm tắt	Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, providing near-optimal solutions with probabilistic quality guarantees. The approach is especially suited for real-world logistics, transportation, and scheduling problems where uncertainty is inherent and classical deterministic solvers fail to capture variability.	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.