Pipe Spool Fabrication Sequencing by Automated Planning
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
Construction of Industrial facilities involves a substantial amount of piping. Pipe spools are usually pre-fabricated from a number of raw pipes and pipe fittings (e.g. elbows, flanges, tees, etc.) in fabrication shops. Pipe spool fabrication is often affected by various disruptions from within or outside the shops. Previous research mainly focuses on shop layouts, dispatching rules, buffer location and standardized products. Another critical factor, the sequencing of pipe spool fabrication, is usually overlooked. A pipe spool can be fabricated in several alternative sequences that are often decided by shop foremen based on experience. It is rare that these alternative sequences get compared and evaluated. A simulation experiment shows that shop productivity can be improved by varying spool fabrication sequence. This paper presents an investigation of Artificial Intelligence (AI) planning approach that automatically identifies the optimal fabrication sequence for pipe spools while considering various fabrication logics. Experiments are conducted with different AI planners to evaluate their capabilities. The results indicate that one of the planners is more suitable for solving the sequencing problem than others. However, it requires special pre-processing of the input that may be prohibiting for practical use. Directions of future research to overcome these limitations are discussed.
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 | 0.001 | 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 | 0.001 |
| 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