Abstract
We investigate a data-driven dynamic inventory control problem involving fixed setup costs and lost sales. Random demand arrivals stem from a demand distribution that is only known to come out of a vast ambiguity set. Lost sales and demand ambiguity would together complicate the problem through censoring, namely, the inability of the firm to observe the lost portion of the demand data. Our main policy idea advocates periodically ordering up to high levels for learning purposes and, in intervening periods, cleverly exploiting the information gained in learning periods. By regret, we mean the price paid for ambiguity in long-run average performances. When demand has a finite support, we can accomplish a regret bound in the order of (Formula presented.) which almost matches a known lower bound as long as inventory costs are genuinely convex. Major policy adjustments are warranted for the more complex case involving an unbounded demand support. The resulting regret could range between (Formula presented.) and (Formula presented.) depending on the nature of moment-related bounds that help characterize the degree of ambiguity. These are improvable to (Formula presented.) when distributions are light-tailed. Our simulation demonstrates the merits of various policy ideas.
| Original language | American English |
|---|---|
| Pages (from-to) | 1220-1236 |
| Number of pages | 17 |
| Journal | Naval Research Logistics |
| Volume | 71 |
| Issue number | 8 |
| DOIs | |
| State | Published - Dec 2024 |
| Externally published | Yes |
ASJC Scopus subject areas
- Modeling and Simulation
- Ocean Engineering
- Management Science and Operations Research
Keywords
- ambiguity
- demand censoring
- fixed setup costs
- inventory
- regret