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.
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 | 0.001 | 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".