Combining a Smart Pricing Policy with a Simple Replenishment Policy: Managing Uncertainties in the Presence of Stochastic Purchase Returns
Bibliographic record
Abstract
This paper addresses operational challenges faced by retailers offering free return policies. We consider a general system with lost sales, positive lead time, periodic review, binomial demand, and an arbitrary restriction on price change frequency. We study the joint pricing and inventory decisions in the presence of stochastic returns. Specifically, when an item is purchased, it can be returned at a future random time and may be restocked for resale after passing an inspection. We assume a general stationary return time distribution. A key challenge in both policy design and analysis arises from the dynamic coupling introduced by returns being restocked over time. To address this, we propose a simple yet effective policy that combines a simple inventory policy with adaptive pricing based on observed sales and returns. Our results provide insights into how uncertainty in both demand and returns can be managed through adaptive pricing under various price change constraints. The analysis can be extended to more general settings, including (1) return fees and partial refunds, (2) nonstationary demand, and (3) service-level constraints. We also show numerically that misspecifying the return time distribution can lead to significant losses, even in a fully deterministic system without randomness. Funding: J. Uichanco was partially supported by the NSF [Grant 2208189]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2022.0172 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".