Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions
Bibliographic record
Abstract
We present an approximate dynamic programming method based on simulation, policy iteration, a postdecision state formulation, and a logistic value function approximation. This method was developed as part of our efforts to determine whether nonlinear value function approximations could provide cost-effective policies for advance patient scheduling problems, and as a way of identifying the main advantages and disadvantages of using simulation versus linear programming to approximately solve dynamic capacity allocation problems. We first apply the proposed method to a queueing problem and then study a more practical application based on an advance multipriority patient scheduling problem. We investigate the quality and practical implications of the resulting appointment scheduling policies using simulation, and compare their performance to that of four other policies. Patient scheduling policies obtained by the new method not only depend on the number of appointments already booked on each day but also on the overall system workload. In particular, these policies provide lower discounted cost values and shorter average wait times for higher priority patients than policies directly obtained using linear programming and an affine value function approximation in the predecision state variables.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".