Truck loading with weight balancing considerations
Why this work is in the frame
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Bibliographic record
Abstract
In order to ensure transportation safety, one main factor in the truck loading problem (TLP) that must be addressed is stability. This paper presents two models of the TLP with weight balancing considerations: 1) balancing the axle weight; 2) balancing the total weight. In the first model, a set of stock keeping units (SKUs) with different weights are loaded on to a pallet, and then the pallets are loaded on to available trucks to minimise the total number of used trucks under the constraints of truck weight limit and axle weight balancing. In the second model, a set of cargoes with different weights are loaded on to a fixed number of trucks with the purpose to balance the loaded weights for all the trucks under the constraints of weight limit. Both models are mixed integer programmes (MIPs). Efficient heuristics are designed to solve these models. Computational results show that the proposed approach can be used to solve the real world TLPs with balancing considerations.
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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 | 0.001 | 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) | 0.001 | 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 it