Optimization of production control policies in failure-prone homogenous transfer lines
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
The production control of homogenous transfer lines with machines that are prone to failure is considered in terms of inventory and backlog costs. Because problem complexity grows with line size, a heuristic method based on the profile of the distribution of buffer capacities in moderate size lines is developed in order to enable the optimization of long lines. A method consisting of an analytical formalism, combined discrete/continuous simulation modeling, design of experiments and response surface methodology is used to optimize a set of transfer lines, with one parameter per machine, for up to seven machines. A profile in the parameter distribution which can be modeled using four-parameters is observed. Consequently, the optimization problem is reduced to four parameters, in turn greatly reducing the required optimization effort. An example of a 20-machine line, optimized at 130 runs, versus 5243 090 runs that would be necessary to solve the 20-parameter problem, is presented to illustrate the usefulness of the parameterized profile.
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