Optimization via Computer Simulation of a Mixed Assembly Line of Wooden Furniture - A Case Study
Bibliographic record
Abstract
In this article, a new model based on Taguchi design and simulation is suggested to study a mixed assembly line of wooden furniture. The experiment contains 9 independent variables, related to product’s mixes and colors, scheduling rules and Product Life Cycle (PLC) and 3 dependent variables related to Lead-time, flow time and setup time. L27 Taguchi’s plan was selected as the appropriate experimental design and the Minitab software was used to carry out its analysis and to obtain its related results. The actual model required the use of many other software, such as: The Enterprise Resources Planning (ERP) system, ‘SQL Server’, ‘Microsoft Report Builder’ and ‘Arena’. This study proves that product mixes, number of offered colors and the scheduling rule for the first sandblast’s workstation have a significant effect on all dependent variables. In addition, scheduling rule for the preparation’s workstation has an important effect on the Lead-time and the flow time. This model can also be a decision support for the line manager. Although PLC’s factors were considered for screening purpose, they turned out to be generally insignificant. This is explained by the similarity between collections of products. In addition, Tukey test, shows that offering sub-categories 1, 2 and 3 and applying the modified shortest processing time’s rule at the preparation’s workstation reduces significantly the mean Lead-time. Finally, it was shown that, there is no difference between offering 22 or 32 colors in term of Lead-time.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".