Name
Title | ||
|---|---|---|
Technical Note - Perishable Inventory Systems: Convexity Results for Base-Stock Policies and Learning Algorithms Under Censored Demand. |
Abstract | ||
|---|---|---|
We develop the first nonparametric learning algorithm for periodic-review perishable inventory systems. In contrast to the classical perishable inventory literature, we assume that the firm does not know the demand distribution a priori and makes replenishment decisions in each period based only on the past sales (censored demand) data. It is well known that even with complete information about the demand distribution a priori, the optimal policy for this problem does not possess a simple structure. Motivated by the studies in the literature showing that base-stock policies perform near optimal in these systems, we focus on finding the best base-stock policy. We first establish a convexity result, showing that the total holding, lost sales and outdating cost is convex in the base-stock level. Then, we develop a nonparametric learning algorithm that generates a sequence of order-up-to levels whose running average cost converges to the cost of the optimal base-stock policy. We establish a square-root conver... |
Year | Venue | Field |
|---|---|---|
2018 | Operations Research | Convexity,Technical note,Algorithm,Nonparametric statistics,Mathematics |
DocType | Volume | Issue |
Journal | 66 | 5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huanan Zhang | 1 | 7 | 3.66 |
| Xiuli Chao | 2 | 287 | 34.24 |
| Cong Shi | 3 | 489 | 40.60 |