Scheduling Multiple Parts in Two-Machine Dual-Gripper Robot Cells: Heuristic Algorithm and Performance Guarantee
Bibliographic record
Abstract
A robotic cell is a manufacturing system that is widely used in industry. Our research concerns scheduling of multiple products in a robotic cell served by a dual-gripper robot. The cell contains two robot-served machines repetitively producing a set of multiple parts in a steady state. The processing constraints specify the cell to be a flow shop. The purpose is to find simultaneously a robot move sequence and a part sequence that minimize the production cycle time or, equivalently, maximize the throughput rate. It is known that the problem of finding an optimal part sequence is strongly NP-hard, even when the robot move sequence is given. The intractable problem of part sequencing in a twomachine dual-gripper robot cell is the main subject of our investigation. We provide a unified notational and modeling framework to study the family of all those NP-hard problems that are associated with the potentially optimal robot move sequences. The main result is the development of an approximation algorithm with a worst-case performance ratio guarantee of 3/2. A linear program is used to establish the performance ratio without actually calculating a lower bound. This approach is original in the literature of scheduling robotic cells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".