Exploring the Potential of Reinforcement Learning in Pipe Spool Scheduling in Industrial Projects
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
Pipe spools are key components in industrial projects.Usually, they are built off-site in a fabrication shop and then shipped to the project location for installation.The fabrication shop deals with numerous spools, each designed to specific requirements according to shop drawings.The nature of pipe spools being engineered to order, together with production constraints such as lead time of materials, different processing times, and availability of resources, render the scheduling process within the shop challenging and time-consuming.As such, this research aims to automate the scheduling process by developing a reinforcement learning model that includes an agent that is capable of handling the scheduling process.The proposed model is applied to an illustrative example to investigate the concept of automating the scheduling process.The construction professionals highlight the great potential of the proposed model in the fabrication scheduling process, and its ability to minimize manual intervention.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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