Spatial Scheduling for Mega-block Assembly Yard in Shipbuilding Company
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
To mitigate space restriction and to raise productivity, some shipbuilding companies use floating-docks on the sea instead of dry-docks on the land. In that case, a floating-crane that can lift very heavy objects (up to 3,600 tons) is used to handle the blocks which are the basic units in shipbuilding processes, and so, very large blocks (these are called the mega-blocks) can be used to build a ship. But, because these mega-blocks can be made only in the area near the floating-dock and beside the sea, the space is very important resource for the process. Therefore, our problem is to make an efficient spatial schedule for the mega-block assembly yard. First of all, we formulate this situation into a mathematical model and find optimal solution for a small problem using a commercial optimization software. But, the software could not give optimal solutions for practical sized problems in a reasonable time, and so we propose a GA-based heuristic algorithm. Through a numerical experiment, finally, we show that the spatial scheduling algorithm can provide a very good performance.
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