Examining Impacts of Future Procurement and Construction Project Schedules on Naval Berthing Capacity by Integrating Constraint Programming and K-Modes Clustering
Bibliographic record
Abstract
The Royal Canadian Navy (RCN) is undergoing the largest recapitalisation of naval assets in its modern history. Over a multi-decade period, several fleets are to be replaced, and naval dockyard infrastructure upgraded, to meet the requirements of the new fleets. Careful coordination of multiple procurement and construction projects is critical to ensure current and future fleets have appropriate berths at in-service jetties during the transition period, and to minimise disruption to RCN operations. Determining whether a proposed schedule permits viable berth plans (i.e., assignments of vessels to berths) for the fleets over the course of several years is a constraint satisfaction problem (CSP). Examples of constraints include vessel safety distances, manoeuvring space requirements, and nesting rules. A multi-stage CSP was developed to identify pressure points in scenarios where no viable berth plans are found. In scenarios where berth plan solutions are found, any caveats that could pose risks to RCN operations are highlighted. To help identify patterns and potential operational risks in berth plan solutions, k-modes clustering, an unsupervised machine learning technique, was integrated into the analysis. This approach yields a representative subset of the full solution space. A visualisation tool was then developed to graphically display the representative solution sets on dockyard map images, enabling quick assessment of the viability of the CSP solutions, and validation of the results by project managers and naval staff.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".