Performance evaluation of flexible manufacturing systems using factorial design techniques
Bibliographic record
Abstract
The authors present the results of a simulation study of a flexible manufacturing system (FMS). The FMS considered includes ten machining centers capable of performing a variety of tasks, an automated guided vehicle (AGV) based material handling system, and an automated single-input-single-output storage-retrieval system connected to the manufacturing system by conveyors. The system manufactures a large part mix and the parts are transported within the system by the AGV on pallets. Alternate part routing, which is an integral feature of most present day FMS, has been included in the model of this system. The model for the present study accounts for uncertainties such as stochastic part arrival patterns, variable machining times, and machine breakdowns. The performance of the system has been investigated under difficult AGV availability conditions, operation time levels, scheduling rules, and layouts of the system using factorial design techniques. The time spent by parts in the system is considered one of the main criteria of system performance. Statistical methods are used to analyze the effects of these parameters on the system performance.>
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".