Scheduling Multiple Parts in Two-Machine Dual-Gripper Robot Cells: Heuristic Algorithm and Performance Guarantee
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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