Stochastic cycle time analysis in robotic cells
Bibliographic record
Abstract
The purpose of this study is to analyse the cycle time in a single part type robotic cell. The robotic cell is made up of several machines and a single gripper robot that loads, unloads the machines and moves the part between machines. The cell can be classified into flow- and open-shops based on the part sequence and robot move. When number of machine is increased, the cycle time analysis in a robotic cell can lead to an NP-complete problem, which remains a challenge in existing literature. In addition, the problem complication rise by having stochastic processing time in the cell. In this study, we have developed a formula for decrease (increase) structure that part processing sequence is based on decrease (increase) order of manufacturing process time. This leads to a virtual robotic cell, which its machines are virtually arranged in order of processing time regardless of their physical location. In addition, using flow-graph concept, a model is developed to calculate the cycle time of a robotic cell where the part processing time element is stochastic. This model provides parametric results for expected value of and variance of the cycle time, which can be used for evaluating the productivity of the scenarios.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".