System Reliability Analysis for a Stochastic Liner Container Shipping Service with Stochastic Terminals
Bibliographic record
Abstract
In the container shipping industry, a company could have an advantage over its competitors while its liner container shipping system (LCSS) can successfully transport the requested demand. However, some uncertainties such as unstable sea state, terrible weather, sailor’s negligence, and the condition of vessels would affect the number of slots on the vessel to place containers when shipping on different legs. Thus, that number should be regarded as stochastic. At the container terminal, containers are loaded from and unloaded to the vessel ship by the quay cranes. The number of available quay cranes at each terminal is also stochastic because of the reservation of other competitors. Therefore, this study proposes an algorithm that constructs an LCSS as a stochastic liner container shipping network (SLCSN) model to analyze the network reliability. Network reliability is defined as the probability that all vessels in the SLCSN can satisfy the demands within the time threshold. For an executive of the liner container shipping company, the network reliability can be utilized as the performance indicator to make an appropriate managerial decision.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".