Optimization of empty container movements using “street-turn” strategy : application to Metro Vancouver area
Bibliographic record
Abstract
This paper focuses on the regional empty container repositioning problem. We propose a more practical model to optimize regional empty container movements. With the framework, we evaluate the effectiveness of “street-turn” strategy and provide insights on “street-turn” operation in Metro Vancouver area. We conduct interviews with local industry professionals to collect information about current empty container operations. The major findings from this research are: (1) “Street-turn” strategy reduces empty container repositioning cost majorly from transportation and gate fees. (2) “Street-turn” strategy is more effective in trade-balanced environment than trade-imbalanced environment. (3) The number of participants in the transport network has a positive impact on the feasibility and effectiveness of “street-turn” strategy. (4) The variance in the supply and demand of empty containers increases the variance in the effectiveness of “street-turn” strategy. (5) Container users have higher incentive to promote “street-turn” operations than shipping lines. (6) “Street-turn” strategy has been conducted jointly by a few importers and exporters in Metro Vancouver area. The major challenge is that container information is not shared among participants. (7) Unlike the situation in LA/LB port region, shipping lines have not yet taken the initiative to promote “street-turn” interchanges in Metro Vancouver area. The successful implementation of “street-turn” strategy depends on the participation of each player. With a high level of information visibility, the proposed model can be employed as a decision support tool to identify “street-turn” opportunities and optimize empty container movements within the system.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".