Buffer clustering policy for sequential production lines with deterministic processing times
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
A sequential production line is defined as a set of sequential operations within a factory or distribution center whereby entities undergo one or more processes to produce a final product. Sequential production lines may gain efficiencies such as increased throughput or reduced work in progress by utilizing specific configurations while maintaining the chronological order of operations. One problem identified by the authors via a case study is that, some of the configurations, such as work cell or U-shaped production lines that have groups of buffers, often increase the space utilization. Therefore, many facilities do not take advantage of the configuration efficiencies that a work cell or U-shaped production line provide. To solve this problem, the authors introduce the concept of a buffer cluster. The production line implemented with one or more buffer clusters maintains the throughput of the line, identical to that with dedicated buffers, but with the clusters reduces the buffer storage space. The paper derives a time based parametric model that determines the sizing of the buffer cluster, provides a reduced time space for which to search for the buffer cluster sizing, and determines an optimal buffer clustering policy that can be applied to any N-server, N+1-buffer sequential line configuration with deterministic processing time. This solution reduces the buffer storage space utilized while ensuring no overflows or underflows occur in the buffer. Furthermore, the paper demonstrates how the buffer clustering policy serves as an input into a facility layout tool that provides the optimal production line layout.
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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 | 0.001 |
| 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