Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle du vendeur de journaux× | Modèles de stock de sécurité et de point de commande× | |
|---|---|---|
| Domaine | Recherche opérationnelle | Recherche opérationnelle |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1951 | 1998 |
| Auteur d'origine≠ | Arrow, Harris & Marschak | Silver, Pyke & Peterson |
| Type≠ | Stochastic single-period inventory optimization | Stochastic inventory control model |
| Source fondatrice≠ | Arrow, K. J., Harris, T., & Marschak, J. (1951). Optimal inventory policy. Econometrica, 19(3), 250–272. DOI ↗ | 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 |
| Alias | Newsboy Model, Single-Period Inventory Model, Christmas Tree Problem, Gazete Satıcısı Modeli | Buffer Stock, Reserve Stock, Reorder-Point Model, Emniyet Stoğu |
| Apparentées | 3 | 3 |
| Résumé≠ | The Newsvendor Model is a single-period stochastic inventory optimization framework that determines the profit-maximizing order quantity when demand is uncertain and unsold units cannot be carried forward. Formally introduced by Arrow, Harris, and Marschak (1951) in their foundational work on optimal inventory policy, the model balances the cost of ordering too much (overage) against the cost of ordering too little (underage) to yield a closed-form optimality condition known as the critical ratio. | 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. |
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