Bibliographic record
Abstract
We consider a maintenance planner problem to dynamically allocate the available repairmen to a system of unreliable production facilities. Each facility has several machines that incur a linear production loss due to stochastic degradation, which we model as a continuous time Markov process with fully observable states. The objective is to schedule group maintenance interventions, in discrete time epochs, so as to minimize production losses over an infinite horizon. Direct solution procedures, such as dynamic programming value or policy iteration, are impractical due to the curse of dimensionality. An approximate scheduling procedure is developed following Whittle’s restless bandits approach. In particular, we decompose the Whittle’s relaxation of our scheduling problem by production facility (i.e., bandit) using the Lagrangian technique. Based on the structural investigation of a single-bandit problem, we prove indexability and propose a novel index computational algorithm. Our numerical study shows that, for systems with three or four facilities, the index policy has a near-zero optimality gap. For systems with 10 or more facilities, the index policy expected cost remains fairly close to a lower bound that we compute using the known linear programming (LP) formulation of Whittle’s relaxation. Furthermore, the numerical study also shows that our policy yields substantial expected cost improvements relative to a benchmark LP-based heuristic when the states are partially observable and can handle large-scale systems unlike LP-based heuristics, which have excessive memory requirements.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".