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| Ilość ekonomiczna zamawiania (EOQ)× | Optymalizacja stochastyczna× | |
|---|---|---|
| Dziedzina≠ | Badania operacyjne | Optymalizacja |
| Rodzina≠ | Regression model | Process / pipeline |
| Rok powstania≠ | 1913 | 1951 (SGD); 2014 (Adam) |
| Twórca≠ | Ford W. Harris | — |
| Typ≠ | Deterministic inventory optimization 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). DOI ↗ | Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407. DOI ↗ |
| Inne nazwy≠ | Wilson EOQ Model, Harris-Wilson Model, Optimal Lot Size Model, Ekonomik Sipariş Miktarı | Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam |
| Pokrewne | 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. | 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. |
| ScholarGateZbiór danych ↗ |
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