Optimization Model and Algorithm of Empty Pallets Dispatching under the Time-Space Network of Express Shipment
Bibliographic record
Abstract
Relying on the express freight network, the dispatching of empty pallets based on the pallet pool mode is studied to reuse pallets with the minimum transport cost, enhance the pallet utilization rate, reduce the waste of resources, and save the cost of logistics. Considering the influence of transport efficiency for different modes in transportation process, differences of transportation cost, carbon emissions, and transportation timeliness of demand points required, an optimization model is constructed. The objective of the model is to minimize the total cost including transportation cost, inventory cost, lease cost, and loss cost. According to the structural characteristics of the model, genetic algorithm and improved cloud clonal selection operation is used to solve the model. Finally, the validity and rationality of the optimization model are verified by a case study. The result shows that the total dispatching cost of considering time requirement is 1.8 times the cost without considering the time requirement, respectively, both less than the total cost of pallets leasing. Moreover, when there are 3 supply points and 2 demand points and the number of iterations is 100, after the algorithms are run for 30 times, the worst values are 9305 and 8317 for genetic algorithm and the improved cloud clonal selection operation, respectively. Therefore, the efficiency of the improved cloud clonal selection operation is higher than genetic algorithm.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".