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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".