Simulation of an Unreliable Production Line
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Bibliographic record
Abstract
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. Cet article simule la capacité d'une ligne de production composée de m stations de travail et (m-1) tampons intermédiaires. Une optimisation expérimentale pour localiser et affecter les tampons est employée afin de maximiser la performance de ce système manufacturier sériel. Les étapes de travail sont présentées sous forme d'un cas qui présente la méthodologie de modélisation par simulation et l’évaluation de l’importance relative de chaque tampon. L’analyse des résultats montre l’échange entre les tampons et le temps de cycle et fournit un guide quant à la meilleure solution. Keywords: simulationproduction linebuffer allocationconjoint analysisMots clés: simulationligne de productiontamponanalyse conjointe
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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 | 0.001 | 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