Optimization of buffer sizes in assembly systems using intelligent techniques
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
When the systems under investigation are complex, the analytical solutions to these systems become impossible. Because of the complex stochastic characteristics of the systems, simulation can be used as an analysis tool to predict the performance of an existing system or a design tool to test new systems under varying circumstances. However, simulation is extremely time consuming for most problems of practical interest. As a result, it is impractical to perform any parametric study of system performance, especially for systems with a large parameter space. One approach to overcome this limitation is to develop a simpler model to explain the relationship between the inputs and outputs of the system. Simulation metamodels are increasingly being used in conjunction with the original simulation, to improve the analysis and understanding of decision-making processes. In this study, an artificial neural network (ANN) metamodel is developed for the simulation model of an asynchronous assembly system and an ANN metamodel together with simulated annealing (SA) is used to optimize the buffer sizes in the system.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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