Platelet Inventory Management with Approximate Dynamic Programming
Bibliographic record
Abstract
We study a stochastic perishable inventory control problem with endogenous (decision-dependent) uncertainty in shelf life of units. Our primary motivation is determining ordering policies for blood platelets. Hospitals typically order their required platelets from a central supplier, and as such, the shelf life of units at the time of delivery can be subject to significant variability. Determining optimal ordering quantities is a challenging task because of the short maximum shelf life of platelets (three to five days after testing) and high uncertainty in daily demand. We formulate the problem as an infinite-horizon discounted Markov decision process (MDP). The model captures salient features observed in our data from a network of Canadian hospitals and allows for fixed ordering costs. We show that with uncertainty in shelf life, the value function of the MDP is nonconvex and key structural properties valid under deterministic shelf life no longer hold. Hence, we propose an approximate dynamic programming (ADP) algorithm to find approximate policies. We approximate the value function using a linear combination of basis functions and tune the parameters using a simulation-based policy iteration algorithm. We evaluate the performance of the proposed policy using extensive numerical experiments in parameter regimes relevant to the platelet inventory management problem. We further leverage the ADP algorithm to evaluate the impact of ignoring shelf-life uncertainty. Finally, we evaluate the out-of-sample performance of the ADP algorithm in a case study using real data and compare it with the historical hospital performance and other benchmarks. After tuning the parameters, the ADP policy can be computed online in a few seconds and results in more than 50% lower expiration and shortage rates compared with the historical rates. In addition, it performs better or as well as other benchmarks, including an exact policy that ignores uncertainty in shelf life and becomes hard to compute for larger instances of the problem. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: V. Sarhangian was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2018-04518]. H. Abouee-Mehrizi was supported by the Natural Sciences and Engineering Council of Canada [Grant RGPIN-2019-05625]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0245 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0245 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".