Inventory Management using Reinforcement Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a significant part of a supply chain, inventory management can involve predicting purchases from customers and controlling order fulfillment. In this thesis, we proposed a Monte Carlo Tree Search (MCTS) algorithm, combined with a local search optimization algorithm for search space reduction and a time series prediction model based on encoder-decoder structure and Bayesian optimization for demand forecasting to tackle an inventory allocation problem from Nestlé Canada, which is a sequential decision problem where we need to minimize the total monthly shortage of supply penalty charged by different companies by suggesting an optimized allocation plan every day with limited inventory on hand and unknown future orders. MCTS is a heuristic search algorithm that has been proved to be powerful in various decision processes, for example board games. In this thesis we introduced a variant of MCTS and showed that MCTS and reinforcement learning can also be effective in inventory management problems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it