Optimal Utilization of Port Free-of-Charge Storage for Non-Instantaneously Deteriorating Items with Time-Varying Order Quantity Dependent Demand with Green Technology Investment during Transit
Bibliographic record
Abstract
This paper presents an innovative inventory model for non-instantaneously deteriorating items with time-varying order quantity-dependent demand. The model strategically utilizes port-provided free storage periods to optimize inventory management across a distribution system comprising one port storage and an owned storage. While most industries rely on owned or rented warehouses to store goods before distributing them to retailers, this model proposes a more cost-efficient approach by leveraging the port’s free storage period as a temporary warehouse until the free duration expires. By investing in energy-efficient green equipment, this approach decreases carbon emissions during product transit between the port and warehouse, as well as to industries. This allows companies to delay the incurrence of holding costs and optimize resource allocation, thereby minimizing total inventory costs while maintaining service levels. The model’s theoretical foundation is verified using numerical examples, which emphasize significant findings on cost optimization and efficient inventory dynamics. Additionally, a comprehensive sensitivity analysis conducted using MATLAB reveals the impact of various parameter modifications, offering valuable insights for decision-makers across diverse industrial settings.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.002 | 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".