方法对比
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| 安全库存与订货点模型× | 随机优化× | |
|---|---|---|
| 领域≠ | 运筹学 | 优化 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 1998 | 1951 (SGD); 2014 (Adam) |
| 提出者≠ | Silver, Pyke & Peterson | — |
| 类型≠ | Stochastic inventory control model | Gradient-based iterative optimization |
| 开创性文献≠ | 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. DOI ↗ |
| 别名≠ | Buffer Stock, Reserve Stock, Reorder-Point Model, Emniyet Stoğu | Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam |
| 相关 | 3 | 3 |
| 摘要≠ | 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. |
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