Inventory model for empty container reposition problem considering quality dependent returns and port capacity constraint
Bibliographic record
Abstract
In this study, an Economic Return Quantity (ERQ) model for the Empty Container Reposition (ECR) problem using the reverse logistics (RL) approach is developed. Some of the model’s primary considerations are the return rate that depends on the quantity and quality of the empty container, and the capacity constraints to hold the empty container in the port. The model of ERQ is optimized using an analytical approach. Based on the result of the hypothetical case, the authors examined that the acceptable quality level of reusable containers should be set at 67%, 55%, and 50% for the three types of containers to be able to obtain minimum inventory costs. Two cases of binding and nonbinding constraints are investigated, and it is found that the binding constraint gives 3.4% higher cost than the latter. The results of this study help the container depots to plan, manage, and handle empty containers so that the container utility can be increased, and inventory costs can be minimized.
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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 | 0.002 | 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".