Crewing of Sea Vessels Taking into Account Project Risks and Technical Condition of Ship Equipment
Bibliographic record
Abstract
Motivation: One of the main concepts in project management is the concept of “team” in the project, and in project management - the human resources management of the project, which includes the processes of planning, forming and creating a team, its development and support activities, transformation or disbandment of the team. Despite the great attention paid to the formation of project management teams, existing studies do not fully highlight the specifics and features of crew operations. Criteria for the quantitative optimization of the ship's crew should be consistent with the main objectives of the project.Novelty: The research paper proposes an approach that allows optimizing the quantitative composition of the crew of a ship by more accurately assessing the level of project risks and costs associated with the maintenance of ship equipment. The practical application of this approach will optimize the quantitative composition of the ship's crew, which will both satisfy the needs of managing the technical equipment and minimize the risks and costs of the shipowner.Methodology and Methods: Risk management tools were used to achieve the objective and test the hypotheses suggested in the research, namely: methodology for estimating the net present value of the project; the method of estimating internal rate of return for the project; the method of estimating the return on investment in the project; the method of estimation for the period of return on investment costs in the project; the method of estimating the discounted payback period for the project, as well as the tools of simulation modelling (Monte Carlo simulation method). The method of identification and grouping in the process of classification of project risks in the sphere of marine transportation, methods of systematization, grouping and logical generalization were also applied for systematization of information, drawing conclusions and making scientific suggestions in the research.Policy Considerations: Shipping plays an important role in the trade and tourism industry; human factor is the most important aspect that determines the efficiency of shipping development; maintaining of technical and technological processes of the ship puts certain requirements to the quantitative and qualitative composition of the team, deviation from which leads to the occurrence of certain risk events; formation of an effective model of ship's crew manning is the main link in ensuring effective shipping project management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".