Loading and scheduling outbound trucks at a dispatch warehouse
Bibliographic record
Abstract
We address the operational planning problem of loading and scheduling outbound trucks at a dispatch warehouse shipping goods to several customers. This entails, first, assigning shipments to outbound trucks given the trailers’ capacities and, second, scheduling the trucks’ processing at the dock doors such that the amount of required resources at the terminal (e.g., dock doors and logistics workers) does not exceed the available levels. The trucks should be scheduled as late as possible within their time windows, but no later than the deadlines of the loaded shipments. Such planning problems arise, e.g., at dispatch warehouses of automotive parts manufacturers supplying parts to original equipment manufacturers in a just-in-time or even just-in-sequence manner. We formalize this operational problem and provide a time-indexed mixed-integer linear programming model. Moreover, we develop an exact branch-and-price algorithm, which is shown to perform very well, solving most realistically sized problem instances to optimality within a few minutes. In a numerical study, we also look into the interplay between the time window policy for trucks and just-in-time deliveries. Finally, we find evidence that too small a workforce or too few outbound dock doors in the dispatch warehouse can substantially compromise the punctuality of the deliveries.
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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 | 0.002 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".