Σύγκριση μεθόδων

	Μοντέλα Αποθέματος Ασφαλείας και Σημείου Αναπαραγγελίας	Στοχαστική Βελτιστοποίηση
Πεδίο	Επιχειρησιακή Έρευνα	Βελτιστοποίηση
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	1998	1951 (SGD); 2014 (Adam)
Δημιουργός	Silver, Pyke & Peterson	—
Τύπος	Stochastic inventory control model	Gradient-based iterative optimization
Θεμελιώδης πηγή	Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (3rd ed.). Wiley. ISBN: 978-0-471-11947-0	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
Εναλλακτικές ονομασίες	Buffer Stock, Reserve Stock, Reorder-Point Model, Emniyet Stoğu	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Συναφείς	3	3
Σύνοψη	Safety stock is an additional quantity of inventory held beyond expected demand during a replenishment lead time, designed to protect against stockouts caused by demand or supply uncertainty. Reorder-point models formalize this buffer by setting a trigger inventory level at which a new order is placed. Systematically developed within the stochastic inventory-control framework by Silver, Pyke, and Peterson (1998), the approach translates a desired customer-service level into a precise buffer quantity using the statistics of demand and lead-time variability.	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.