Simulation modeling as a decision analysis support tool at the Vancouver Container Terminal
Bibliographic record
Abstract
The objective of this research is to find whether replacing tractor/trailers in Vanterm (Vancouver Container Terminal) with straddle carriers will increase the productivity. The productivity is measured in lifts per hour per crane. After a significant productivity increase was demonstrated, the objective of this work was then extended to estimate the optimal number of straddle carriers and to quantify the potential of the straddle carriers in terms of productivity increases. The results of this project will be used to support the decision of purchasing and implementing new equipment for Vanterm. Two discrete-event simulation models were developed as a decision support tool in this project. The models were used to evaluate several transporter allocation scenarios. Statistical analyses were implemented to analyze the results of those scenarios. The results of the simulation gave valuable insight into the vessel operation of Vanterm and provided management at TSI with a strong tool for testing configuration changes to Vanterm without costly investment. In addition to the simulation models, further studies were conducted by testing more scenarios with modified simulation models, applying analytical models and analyzing deterministic models.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".