Research on Multi-Objective Optimisation-Based Task Package Division Methods for Shipbuilding
Bibliographic record
Abstract
Shipbuilding requires converting multi-disciplinary product units—such as hull structures, outfitting equipment, and coating processes—into executable task packages to support sectional construction and final assembly on the slipway. Three critical issues arise during practical division: firstly, weak inter-process connectivity within task packages, where splitting welding and assembly tasks within the same compartment disrupts workflow continuity and increases redundant handling costs; Secondly, high coupling between task packages arises when construction packages are empirically divided, placing sequentially dependent tasks under different crews and triggering frequent cross-departmental coordination. Thirdly, resource allocation imbalances occur when critical resources like machining equipment are concentrated or scarce, constraining the construction cycle. Therefore, this paper constructs a multi-objective optimisation mixed-integer programming model. First, leveraging information entropy theory, we quantify the homogeneity of shipbuilding processes within task packages—such as hull welding and piping pre-installation—to ensure process continuity and specialised concentration. Second, a task dependency matrix quantitatively assesses and reduces inter-package coupling, minimising cross-package coordination costs. Finally, an equitable resource allocation metric is introduced, using variance in critical resource utilisation to achieve balanced distribution. Simultaneously, linearisation techniques address non-linear constraints, while a two-stage solution strategy combining branch-and-bound with genetic algorithms balances accuracy and efficiency. Ultimately, through case validation at a major Chinese shipbuilding enterprise, the proposed task package segmentation methodology demonstrated both its efficacy and practical feasibility. Empirical results demonstrate that this approach offers significant advantages in practical application. It enhances the cohesion of similar processes within task packages, effectively reduces the frequency of cross-package task coordination, and reasonably controls fluctuations in critical resource utilisation. Furthermore, it adapts to the complex scenarios of multi-disciplinary and multi-resource shipbuilding, providing a scientific decision-making tool for project task organisation and resource allocation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".