A Linear Programming Approach to Optimization of Ship Design and Construction Phases
Bibliographic record
Abstract
Labor cost savings can be obtained by considering the effect of design rework on the degree of overlap of the ship design and construction phases. Based on data from a shipbuilding case study, a linear programming (LP) model was developed to investigate the optimum overlap of the design and construction phases. Two scenarios were modeled. The case study's start-up period, which involved design and the construction of four ships, and a hypothetical small batch program to determine the degree of overlap and total hours required for a new shipbuilding program. In each scenario, the LP model found the optimum overlap period for design and construction and the associated total hours. In the first scenario, the analysis demonstrates that by reducing the amount of overlap between the design and construction phases, a reduction in construction direct labor hours can be achieved while obtaining the overall duration and scheduled completion times. In the second case, the analysis recommends a zero overlap policy between the design and construction phases to minimize total hours while achieving the overall schedule duration. When compared with the actual case study results, the zero overlap policy estimates an $8 million saving. The analysis supports the policy to complete design activities with construction-dependency relationships before starting construction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".