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	Ilość ekonomiczna zamawiania (EOQ)	Modele zapasu bezpieczeństwa i punktu ponownego zamawiania	Optymalizacja stochastyczna
Dziedzina	Badania operacyjne	Badania operacyjne	Optymalizacja
Rodzina	Regression model	Regression model	Process / pipeline
Rok powstania	1913	1998	1951 (SGD); 2014 (Adam)
Twórca	Ford W. Harris	Silver, Pyke & Peterson	—
Typ	Deterministic inventory optimization model	Stochastic inventory control model	Gradient-based iterative optimization
Źródło pierwotne	Harris, F. W. (1913/1990). How many parts to make at once. Operations Research, 38(6), 947–950 (reprint).	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.
Inne nazwy	Wilson EOQ Model, Harris-Wilson Model, Optimal Lot Size Model, Ekonomik Sipariş Miktarı	Buffer Stock, Reserve Stock, Reorder-Point Model, Emniyet Stoğu	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Pokrewne	3	3	3
Podsumowanie	The Economic Order Quantity (EOQ) is a classic deterministic inventory model that identifies the order quantity minimizing the sum of annual ordering and holding costs. Introduced by Ford W. Harris in 1913 and later popularized by R. H. Wilson, EOQ assumes constant demand, fixed cost parameters, and instantaneous replenishment. It remains the foundational benchmark for inventory management in manufacturing, retail, and supply chain contexts where demand is relatively stable and costs are well-characterized.	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.