Stochastic optimization applied to a manufacturing system operation problem
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
This paper deals with stochastic optimization of a discrete-event simulation model for the solution of a manufacturing system operation problem. Gradient estimates are obtained by the application of the infinitesimal perturbation analysis (IPA) technique. We begin with background material on stochastic approximation (SA) and the IPA technique, their potential value in finding optimal solutions to manufacturing system operation problems, and limitations concerning their applicability. Next we present our attempt to solve a real problem (the design of a partially-automated assembly line in an electronics manufacturing facility) using this approach. A sequence of models is described moving from one which embodies some restrictive assumptions through to models which more closely approximate the real system. All of the models are implemented in the SIMAN IV simulation language incorporating user-written code (written in C++) implementing the SA and IPA algorithms. We report and interpret the results obtained with the different models and close with concluding remarks on the current value of this technique in solving this kind of system design problem.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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