Bibliographic record
Abstract
This paper presents a simulation model to evaluate the capacity of a multi-product unreliable production line composed of m workstations and (m-1) intermediate buffers. An experimental optimization to test the deployment of buffers between workstations is used to evaluate the maximum contribution of buffers on the overall performance of the considered manufacturing system. A case study consisting of four workstations and three buffers is presented to show all the steps involved in simulation modelling and in the evaluation of the relative importance of each buffer. The purpose of this work is to address the design of the production line by taking into account the various parameters that affect the performance of the production line such as random failure and repair of workstations, the variety of products, the set-up time of workstations as product type changes, and buffer’s deployment. Analysis of the results shows the trade-offs between the different buffers and the cycle time of the production line. Based on the conjoint analysis procedure, the results report the relative importance of each buffer and give insights about the most influential buffers to achieve a minimum cycle time so that a managerial decision regarding the most viable or relevant solution can be chosen at a glance.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".